{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e61b524",
   "metadata": {},
   "source": [
    "# 基于视线和表情数据的自闭症(ASD)与正常发育(TD)儿童分类研究\n",
    "\n",
    "本项目旨在通过分析儿童的视线方向(Gaze_X, Gaze_Y)和面部表情(Expression)数据，建立一个能够区分自闭症(ASD)患者和正常发育(TD)儿童的分类模型。\n",
    "\n",
    "数据来源：\n",
    "- ASD文件夹：包含自闭症患者的数据\n",
    "- TD文件夹：包含正常发育儿童的数据\n",
    "\n",
    "每个CSV文件代表一个受试者（一个样本），包含以下数据列：\n",
    "- Frame：视频帧数\n",
    "- Gaze_X, Gaze_Y：视线方向坐标\n",
    "- Expression：面部表情编码\n",
    "\n",
    "我们将采用多种机器学习算法进行分类，并比较它们的性能。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b11c8632",
   "metadata": {},
   "source": [
    "## 1. 导入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "96c8bbab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "# 机器学习相关库\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier, AdaBoostClassifier\n",
    "\n",
    "# 设置随机种子，确保结果可复现\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "efc3b249",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系统中的字体数量: 402\n",
      "找到可用的中文字体: SimHei\n",
      "已将 SimHei 设为默认字体\n",
      "\n",
      "系统中的部分字体:\n",
      "Agency FB\n",
      "Agency FB\n",
      "Algerian\n",
      "Arial\n",
      "Arial\n",
      "Arial\n",
      "Arial\n",
      "Arial\n",
      "Arial\n",
      "Arial\n",
      "Arial\n",
      "Arial\n",
      "Arial Rounded MT Bold\n",
      "Bahnschrift\n",
      "Baskerville Old Face\n",
      "Bauhaus 93\n",
      "Bell MT\n",
      "Bell MT\n",
      "Bell MT\n",
      "Berlin Sans FB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 检查系统中可用的字体并设置中文字体支持\n",
    "from matplotlib.font_manager import fontManager\n",
    "\n",
    "# 尝试更全面的中文字体配置\n",
    "def setup_chinese_font():\n",
    "    \"\"\"配置matplotlib使用中文字体\"\"\"\n",
    "    # 常见的中文字体，按优先级排序\n",
    "    chinese_fonts = [\n",
    "        'SimHei',        # 黑体\n",
    "        'Microsoft YaHei', # 微软雅黑\n",
    "        'SimSun',        # 宋体\n",
    "        'FangSong',      # 仿宋\n",
    "        'KaiTi',         # 楷体\n",
    "        'STSong',        # 华文宋体\n",
    "        'STKaiti',       # 华文楷体\n",
    "        'STFangsong',    # 华文仿宋\n",
    "        'STHeiti',       # 华文黑体\n",
    "        'Source Han Sans CN', # 思源黑体\n",
    "        'Source Han Serif CN', # 思源宋体\n",
    "        'Noto Sans CJK SC', # Noto字体\n",
    "        'Noto Serif CJK SC',\n",
    "        'WenQuanYi Micro Hei', # 文泉驿字体\n",
    "        'WenQuanYi Zen Hei',\n",
    "        'Hiragino Sans GB'  # 冬青黑体\n",
    "    ]\n",
    "    \n",
    "    # 获取系统中所有字体的名称\n",
    "    all_fonts = [f.name for f in fontManager.ttflist]\n",
    "    print(\"系统中的字体数量:\", len(all_fonts))\n",
    "    \n",
    "    # 查找第一个可用的中文字体\n",
    "    chinese_font = None\n",
    "    for font in chinese_fonts:\n",
    "        if font in all_fonts:\n",
    "            chinese_font = font\n",
    "            print(f\"找到可用的中文字体: {font}\")\n",
    "            break\n",
    "    \n",
    "    if chinese_font:\n",
    "        plt.rcParams['font.sans-serif'] = [chinese_font, 'DejaVu Sans', 'Arial']\n",
    "        plt.rcParams['axes.unicode_minus'] = False\n",
    "        print(f\"已将 {chinese_font} 设为默认字体\")\n",
    "    else:\n",
    "        print(\"未找到可用的中文字体，将尝试使用系统默认字体\")\n",
    "        # 尝试使用系统默认字体\n",
    "        plt.rcParams['font.sans-serif'] = ['DejaVu Sans', 'Arial'] + plt.rcParams['font.sans-serif']\n",
    "        \n",
    "    # 显示部分可用的字体\n",
    "    print(\"\\n系统中的部分字体:\")\n",
    "    for font in sorted(all_fonts)[:20]:  # 只显示前20个\n",
    "        print(font)\n",
    "    \n",
    "    return chinese_font\n",
    "\n",
    "# 执行字体配置\n",
    "selected_font = setup_chinese_font()\n",
    "\n",
    "# 测试中文显示\n",
    "plt.figure(figsize=(6, 2))\n",
    "plt.text(0.5, 0.5, '中文显示测试 - Test Chinese Font', \n",
    "         ha='center', va='center', fontsize=16)\n",
    "plt.title('字体测试 - Font Test')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c7a868",
   "metadata": {},
   "source": [
    "## 2. 加载并预处理数据\n",
    "\n",
    "接下来我们将加载ASD和TD文件夹中的所有CSV文件，并进行初步预处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "537707d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "找到124个ASD样本文件\n",
      "找到145个TD样本文件\n",
      "\n",
      "ASD样本文件示例:\n",
      "BSDASD_1.csv\n",
      "BSDASD_10.csv\n",
      "BSDASD_11.csv\n",
      "BSDASD_12.csv\n",
      "BSDASD_13.csv\n",
      "\n",
      "TD样本文件示例:\n",
      "DCXX_1.csv\n",
      "DCXX_10.csv\n",
      "DCXX_12.csv\n",
      "DCXX_13.csv\n",
      "DCXX_14.csv\n"
     ]
    }
   ],
   "source": [
    "# 定义数据路径\n",
    "base_path = r\"d:\\360Downloads\\python数据分析\"\n",
    "asd_path = os.path.join(base_path, \"ASD\")\n",
    "td_path = os.path.join(base_path, \"TD\")\n",
    "\n",
    "# 获取所有ASD样本文件路径\n",
    "asd_files = glob(os.path.join(asd_path, \"*.csv\"))\n",
    "print(f\"找到{len(asd_files)}个ASD样本文件\")\n",
    "\n",
    "# 获取所有TD样本文件路径\n",
    "td_files = glob(os.path.join(td_path, \"*.csv\"))\n",
    "print(f\"找到{len(td_files)}个TD样本文件\")\n",
    "\n",
    "# 查看部分文件名\n",
    "print(\"\\nASD样本文件示例:\")\n",
    "for file in asd_files[:5]:\n",
    "    print(os.path.basename(file))\n",
    "\n",
    "print(\"\\nTD样本文件示例:\")\n",
    "for file in td_files[:5]:\n",
    "    print(os.path.basename(file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8b4708a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "第一个ASD样本数据结构:\n",
      "行数: 1309, 列数: 4\n",
      "   Frame    Gaze_X    Gaze_Y  Expression\n",
      "0      1 -0.235635  0.119666           6\n",
      "1      2 -0.175542  0.193935           6\n",
      "2      3 -0.231333  0.256052           6\n",
      "3      4 -0.152009  0.225839           6\n",
      "4      5 -0.240985  0.268436           6\n",
      "表情(Expression)类型: [6 4 0 2 3]\n",
      "\n",
      "第一个TD样本数据结构:\n",
      "行数: 2699, 列数: 4\n",
      "   Frame    Gaze_X    Gaze_Y  Expression\n",
      "0      1 -0.152347  0.170721           6\n",
      "1      2 -0.154296  0.157409           4\n",
      "2      3 -0.057838  0.171644           6\n",
      "3      4 -0.134995  0.115320           6\n",
      "4      5 -0.099205  0.146463           4\n",
      "表情(Expression)类型: [6 4 2 0]\n",
      "\n",
      "ASD样本帧数统计:\n",
      "最小帧数: 506\n",
      "最大帧数: 3182\n",
      "平均帧数: 2309.77\n",
      "中位数帧数: 2335.5\n",
      "\n",
      "TD样本帧数统计:\n",
      "最小帧数: 632\n",
      "最大帧数: 3103\n",
      "平均帧数: 2391.13\n",
      "中位数帧数: 2431.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看第一个ASD样本的数据结构\n",
    "asd_sample = pd.read_csv(asd_files[0])\n",
    "print(f\"\\n第一个ASD样本数据结构:\")\n",
    "print(f\"行数: {asd_sample.shape[0]}, 列数: {asd_sample.shape[1]}\")\n",
    "print(asd_sample.head())\n",
    "print(f\"表情(Expression)类型: {asd_sample['Expression'].unique()}\")\n",
    "\n",
    "# 查看第一个TD样本的数据结构\n",
    "td_sample = pd.read_csv(td_files[0])\n",
    "print(f\"\\n第一个TD样本数据结构:\")\n",
    "print(f\"行数: {td_sample.shape[0]}, 列数: {td_sample.shape[1]}\")\n",
    "print(td_sample.head())\n",
    "print(f\"表情(Expression)类型: {td_sample['Expression'].unique()}\")\n",
    "\n",
    "# 检查每个样本的帧数\n",
    "asd_frames = []\n",
    "for file in asd_files:\n",
    "    df = pd.read_csv(file)\n",
    "    asd_frames.append(df.shape[0])\n",
    "\n",
    "td_frames = []\n",
    "for file in td_files:\n",
    "    df = pd.read_csv(file)\n",
    "    td_frames.append(df.shape[0])\n",
    "\n",
    "print(f\"\\nASD样本帧数统计:\")\n",
    "print(f\"最小帧数: {min(asd_frames)}\")\n",
    "print(f\"最大帧数: {max(asd_frames)}\")\n",
    "print(f\"平均帧数: {np.mean(asd_frames):.2f}\")\n",
    "print(f\"中位数帧数: {np.median(asd_frames)}\")\n",
    "\n",
    "print(f\"\\nTD样本帧数统计:\")\n",
    "print(f\"最小帧数: {min(td_frames)}\")\n",
    "print(f\"最大帧数: {max(td_frames)}\")\n",
    "print(f\"平均帧数: {np.mean(td_frames):.2f}\")\n",
    "print(f\"中位数帧数: {np.median(td_frames)}\")\n",
    "\n",
    "# 可视化帧数分布\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(asd_frames, bins=20, alpha=0.7, label='ASD')\n",
    "plt.title('ASD样本帧数分布')\n",
    "plt.xlabel('帧数')\n",
    "plt.ylabel('样本数量')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(td_frames, bins=20, alpha=0.7, label='TD')\n",
    "plt.title('TD样本帧数分布')\n",
    "plt.xlabel('帧数')\n",
    "plt.ylabel('样本数量')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ffd212",
   "metadata": {},
   "source": [
    "## 3. 样本帧数统一与特征提取\n",
    "\n",
    "由于不同样本的帧数不一致，我们需要：\n",
    "1. 统一每个样本的帧数（取前2000帧）\n",
    "2. 提取每个样本的特征\n",
    "\n",
    "我们将从每个样本中提取以下特征：\n",
    "- 视线坐标的统计特征（均值、标准差、最大值、最小值等）\n",
    "- 面部表情的统计特征（各种表情的出现频率）\n",
    "- 视线移动特征（移动速度、加速度等）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ea137a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义一个函数来提取单个样本的特征\n",
    "def extract_features(file_path, max_frames=2000):\n",
    "    \"\"\"\n",
    "    从一个CSV文件中提取特征\n",
    "    \n",
    "    参数:\n",
    "    file_path: CSV文件路径\n",
    "    max_frames: 要保留的最大帧数\n",
    "    \n",
    "    返回:\n",
    "    一个包含提取特征的字典\n",
    "    \"\"\"\n",
    "    # 读取CSV文件\n",
    "    df = pd.read_csv(file_path)\n",
    "    \n",
    "    # 只保留前max_frames帧\n",
    "    df = df.head(max_frames)\n",
    "    \n",
    "    # 如果帧数不足max_frames，填充缺失值\n",
    "    if df.shape[0] < max_frames:\n",
    "        # 创建缺失的行\n",
    "        missing_rows = max_frames - df.shape[0]\n",
    "        # 使用最后一行的数据填充缺失值\n",
    "        last_row = df.iloc[-1].to_dict()\n",
    "        missing_data = pd.DataFrame([last_row] * missing_rows)\n",
    "        # 连接原始数据和填充数据\n",
    "        df = pd.concat([df, missing_data], ignore_index=True)\n",
    "    \n",
    "    # 提取视线坐标的统计特征\n",
    "    gaze_x_mean = df['Gaze_X'].mean()\n",
    "    gaze_x_std = df['Gaze_X'].std()\n",
    "    gaze_x_min = df['Gaze_X'].min()\n",
    "    gaze_x_max = df['Gaze_X'].max()\n",
    "    gaze_x_range = gaze_x_max - gaze_x_min\n",
    "    gaze_x_median = df['Gaze_X'].median()\n",
    "    \n",
    "    gaze_y_mean = df['Gaze_Y'].mean()\n",
    "    gaze_y_std = df['Gaze_Y'].std()\n",
    "    gaze_y_min = df['Gaze_Y'].min()\n",
    "    gaze_y_max = df['Gaze_Y'].max()\n",
    "    gaze_y_range = gaze_y_max - gaze_y_min\n",
    "    gaze_y_median = df['Gaze_Y'].median()\n",
    "    \n",
    "    # 计算视线移动特征（速度和加速度）\n",
    "    df['Gaze_X_diff'] = df['Gaze_X'].diff().fillna(0)\n",
    "    df['Gaze_Y_diff'] = df['Gaze_Y'].diff().fillna(0)\n",
    "    \n",
    "    # 计算视线移动速度\n",
    "    df['Gaze_speed'] = np.sqrt(df['Gaze_X_diff']**2 + df['Gaze_Y_diff']**2)\n",
    "    \n",
    "    # 提取视线速度特征\n",
    "    gaze_speed_mean = df['Gaze_speed'].mean()\n",
    "    gaze_speed_std = df['Gaze_speed'].std()\n",
    "    gaze_speed_max = df['Gaze_speed'].max()\n",
    "    gaze_speed_median = df['Gaze_speed'].median()\n",
    "    \n",
    "    # 计算视线加速度\n",
    "    df['Gaze_accel'] = df['Gaze_speed'].diff().fillna(0)\n",
    "    \n",
    "    # 提取视线加速度特征\n",
    "    gaze_accel_mean = df['Gaze_accel'].mean()\n",
    "    gaze_accel_std = df['Gaze_accel'].std()\n",
    "    gaze_accel_max = df['Gaze_accel'].max()\n",
    "    gaze_accel_min = df['Gaze_accel'].min()\n",
    "    \n",
    "    # 提取面部表情特征\n",
    "    expression_counts = df['Expression'].value_counts().to_dict()\n",
    "    # 确保所有可能的表情类型都有计数（如果缺少某种表情，则计数为0）\n",
    "    for i in range(1, 8):  # 假设表情编码为1-7\n",
    "        if i not in expression_counts:\n",
    "            expression_counts[i] = 0\n",
    "    \n",
    "    # 计算各表情出现的比例\n",
    "    expression_ratio = {f'Expression_{i}_ratio': expression_counts.get(i, 0) / max_frames \n",
    "                        for i in range(1, 8)}\n",
    "    \n",
    "    # 计算表情变化频率\n",
    "    expression_changes = (df['Expression'].diff() != 0).sum() / max_frames\n",
    "    \n",
    "    # 构建特征字典\n",
    "    features = {\n",
    "        'gaze_x_mean': gaze_x_mean,\n",
    "        'gaze_x_std': gaze_x_std,\n",
    "        'gaze_x_min': gaze_x_min,\n",
    "        'gaze_x_max': gaze_x_max,\n",
    "        'gaze_x_range': gaze_x_range,\n",
    "        'gaze_x_median': gaze_x_median,\n",
    "        \n",
    "        'gaze_y_mean': gaze_y_mean,\n",
    "        'gaze_y_std': gaze_y_std,\n",
    "        'gaze_y_min': gaze_y_min,\n",
    "        'gaze_y_max': gaze_y_max,\n",
    "        'gaze_y_range': gaze_y_range,\n",
    "        'gaze_y_median': gaze_y_median,\n",
    "        \n",
    "        'gaze_speed_mean': gaze_speed_mean,\n",
    "        'gaze_speed_std': gaze_speed_std,\n",
    "        'gaze_speed_max': gaze_speed_max,\n",
    "        'gaze_speed_median': gaze_speed_median,\n",
    "        \n",
    "        'gaze_accel_mean': gaze_accel_mean,\n",
    "        'gaze_accel_std': gaze_accel_std,\n",
    "        'gaze_accel_max': gaze_accel_max,\n",
    "        'gaze_accel_min': gaze_accel_min,\n",
    "        \n",
    "        'expression_changes': expression_changes,\n",
    "    }\n",
    "    \n",
    "    # 添加表情比例特征\n",
    "    features.update(expression_ratio)\n",
    "    \n",
    "    # 添加样本文件名\n",
    "    features['filename'] = os.path.basename(file_path)\n",
    "    \n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cbe785c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正在处理ASD样本...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 124/124 [00:01<00:00, 108.08it/s]\n",
      "100%|██████████| 124/124 [00:01<00:00, 108.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "正在处理TD样本...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 145/145 [00:01<00:00, 103.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "成功处理 124 个ASD样本\n",
      "成功处理 145 个TD样本\n",
      "\n",
      "提取的特征列: ['gaze_x_mean', 'gaze_x_std', 'gaze_x_min', 'gaze_x_max', 'gaze_x_range', 'gaze_x_median', 'gaze_y_mean', 'gaze_y_std', 'gaze_y_min', 'gaze_y_max', 'gaze_y_range', 'gaze_y_median', 'gaze_speed_mean', 'gaze_speed_std', 'gaze_speed_max', 'gaze_speed_median', 'gaze_accel_mean', 'gaze_accel_std', 'gaze_accel_max', 'gaze_accel_min', 'expression_changes', 'Expression_1_ratio', 'Expression_2_ratio', 'Expression_3_ratio', 'Expression_4_ratio', 'Expression_5_ratio', 'Expression_6_ratio', 'Expression_7_ratio', 'filename']\n",
      "\n",
      "ASD样本特征统计信息:\n",
      "                    count      mean       std           min       25%  \\\n",
      "gaze_x_mean         124.0 -0.328128  0.037533 -4.077984e-01 -0.354227   \n",
      "gaze_x_std          124.0  0.054246  0.013362  2.263081e-02  0.045626   \n",
      "gaze_x_min          124.0 -0.465983  0.032560 -5.589380e-01 -0.491239   \n",
      "gaze_x_max          124.0 -0.077168  0.045712 -2.081150e-01 -0.103306   \n",
      "gaze_x_range        124.0  0.388815  0.056768  2.079179e-01  0.353554   \n",
      "gaze_x_median       124.0 -0.333966  0.042154 -4.212993e-01 -0.362950   \n",
      "gaze_y_mean         124.0  0.049392  0.052482 -1.670209e-01  0.018939   \n",
      "gaze_y_std          124.0  0.101308  0.020588  6.723968e-02  0.083703   \n",
      "gaze_y_min          124.0 -0.280447  0.053002 -4.131962e-01 -0.316449   \n",
      "gaze_y_max          124.0  0.352632  0.060449  7.065362e-02  0.313062   \n",
      "gaze_y_range        124.0  0.633079  0.090349  4.178446e-01  0.563515   \n",
      "gaze_y_median       124.0  0.055208  0.060314 -1.793236e-01  0.016609   \n",
      "gaze_speed_mean     124.0  0.069800  0.013730  1.780690e-02  0.063981   \n",
      "gaze_speed_std      124.0  0.052305  0.011285  3.419889e-02  0.044012   \n",
      "gaze_speed_max      124.0  0.400402  0.070968  2.466754e-01  0.351278   \n",
      "gaze_speed_median   124.0  0.057106  0.012199  0.000000e+00  0.052741   \n",
      "gaze_accel_mean     124.0  0.000030  0.000027 -4.996004e-19  0.000008   \n",
      "gaze_accel_std      124.0  0.063440  0.014126  3.293196e-02  0.054345   \n",
      "gaze_accel_max      124.0  0.335075  0.073081  1.925788e-01  0.277031   \n",
      "gaze_accel_min      124.0 -0.330789  0.075679 -5.307175e-01 -0.379745   \n",
      "expression_changes  124.0  0.253044  0.091246  3.250000e-02  0.192875   \n",
      "Expression_1_ratio  124.0  0.003802  0.007375  0.000000e+00  0.000000   \n",
      "Expression_2_ratio  124.0  0.016258  0.059389  0.000000e+00  0.000000   \n",
      "Expression_3_ratio  124.0  0.037460  0.067397  0.000000e+00  0.002375   \n",
      "Expression_4_ratio  124.0  0.501480  0.250277  7.550000e-02  0.294875   \n",
      "Expression_5_ratio  124.0  0.000121  0.000453  0.000000e+00  0.000000   \n",
      "Expression_6_ratio  124.0  0.383609  0.229182  1.550000e-02  0.161375   \n",
      "Expression_7_ratio  124.0  0.000000  0.000000  0.000000e+00  0.000000   \n",
      "\n",
      "                         50%       75%       max  \n",
      "gaze_x_mean        -0.332843 -0.304377 -0.203978  \n",
      "gaze_x_std          0.053840  0.062218  0.093590  \n",
      "gaze_x_min         -0.467130 -0.444457 -0.396728  \n",
      "gaze_x_max         -0.081503 -0.053255  0.059241  \n",
      "gaze_x_range        0.391835  0.419277  0.522962  \n",
      "gaze_x_median      -0.340385 -0.308955 -0.177747  \n",
      "gaze_y_mean         0.056946  0.083876  0.136952  \n",
      "gaze_y_std          0.099710  0.115323  0.170769  \n",
      "gaze_y_min         -0.277746 -0.245696 -0.161018  \n",
      "gaze_y_max          0.353383  0.384423  0.530278  \n",
      "gaze_y_range        0.629889  0.684187  0.925416  \n",
      "gaze_y_median       0.062147  0.095726  0.160394  \n",
      "gaze_speed_mean     0.068253  0.076184  0.112013  \n",
      "gaze_speed_std      0.049543  0.058344  0.098556  \n",
      "gaze_speed_max      0.391158  0.440308  0.631523  \n",
      "gaze_speed_median   0.057984  0.063328  0.089217  \n",
      "gaze_accel_mean     0.000022  0.000045  0.000112  \n",
      "gaze_accel_std      0.061026  0.071663  0.125685  \n",
      "gaze_accel_max      0.322541  0.376765  0.565834  \n",
      "gaze_accel_min     -0.320674 -0.274441 -0.154134  \n",
      "expression_changes  0.275250  0.320750  0.414000  \n",
      "Expression_1_ratio  0.001000  0.003500  0.037500  \n",
      "Expression_2_ratio  0.001000  0.004000  0.350000  \n",
      "Expression_3_ratio  0.013750  0.045625  0.451500  \n",
      "Expression_4_ratio  0.487750  0.693750  0.974500  \n",
      "Expression_5_ratio  0.000000  0.000000  0.003500  \n",
      "Expression_6_ratio  0.387750  0.562000  0.899000  \n",
      "Expression_7_ratio  0.000000  0.000000  0.000000  \n",
      "\n",
      "TD样本特征统计信息:\n",
      "                    count      mean       std           min       25%  \\\n",
      "gaze_x_mean         145.0 -0.358650  0.038061 -4.330671e-01 -0.387618   \n",
      "gaze_x_std          145.0  0.041842  0.014201  1.945225e-02  0.031004   \n",
      "gaze_x_min          145.0 -0.471219  0.038248 -5.555667e-01 -0.500080   \n",
      "gaze_x_max          145.0 -0.134501  0.084461 -3.308185e-01 -0.174348   \n",
      "gaze_x_range        145.0  0.336717  0.089466  1.383977e-01  0.292353   \n",
      "gaze_x_median       145.0 -0.363437  0.038042 -4.380767e-01 -0.391976   \n",
      "gaze_y_mean         145.0  0.057385  0.049494 -7.135240e-02  0.025391   \n",
      "gaze_y_std          145.0  0.077123  0.017892  4.235214e-02  0.065615   \n",
      "gaze_y_min          145.0 -0.227018  0.072123 -3.720001e-01 -0.276067   \n",
      "gaze_y_max          145.0  0.338216  0.065042  1.696896e-01  0.301443   \n",
      "gaze_y_range        145.0  0.565234  0.111006  2.904527e-01  0.492780   \n",
      "gaze_y_median       145.0  0.057348  0.051339 -8.068288e-02  0.024736   \n",
      "gaze_speed_mean     145.0  0.060908  0.009314  2.179297e-02  0.055466   \n",
      "gaze_speed_std      145.0  0.040628  0.006558  2.630599e-02  0.036062   \n",
      "gaze_speed_max      145.0  0.319748  0.073203  1.705251e-01  0.281140   \n",
      "gaze_speed_median   145.0  0.052463  0.009145  0.000000e+00  0.048303   \n",
      "gaze_accel_mean     145.0  0.000031  0.000022 -1.283695e-19  0.000016   \n",
      "gaze_accel_std      145.0  0.050894  0.008360  3.019484e-02  0.045461   \n",
      "gaze_accel_max      145.0  0.258771  0.067993  1.185947e-01  0.212017   \n",
      "gaze_accel_min      145.0 -0.260231  0.068265 -4.823416e-01 -0.304401   \n",
      "expression_changes  145.0  0.213766  0.104142  3.500000e-03  0.117500   \n",
      "Expression_1_ratio  145.0  0.000610  0.001711  0.000000e+00  0.000000   \n",
      "Expression_2_ratio  145.0  0.001828  0.016710  0.000000e+00  0.000000   \n",
      "Expression_3_ratio  145.0  0.008276  0.019426  0.000000e+00  0.000000   \n",
      "Expression_4_ratio  145.0  0.596228  0.282874  4.000000e-03  0.372500   \n",
      "Expression_5_ratio  145.0  0.000021  0.000100  0.000000e+00  0.000000   \n",
      "Expression_6_ratio  145.0  0.365086  0.272069  2.500000e-03  0.120500   \n",
      "Expression_7_ratio  145.0  0.000000  0.000000  0.000000e+00  0.000000   \n",
      "\n",
      "                         50%       75%       max  \n",
      "gaze_x_mean        -0.359147 -0.330803 -0.227326  \n",
      "gaze_x_std          0.039215  0.049045  0.083187  \n",
      "gaze_x_min         -0.475021 -0.450402 -0.362575  \n",
      "gaze_x_max         -0.121938 -0.077242  0.058980  \n",
      "gaze_x_range        0.335643  0.395568  0.545339  \n",
      "gaze_x_median      -0.365201 -0.337191 -0.231246  \n",
      "gaze_y_mean         0.058820  0.089287  0.211847  \n",
      "gaze_y_std          0.075246  0.087978  0.121036  \n",
      "gaze_y_min         -0.229556 -0.184124 -0.063808  \n",
      "gaze_y_max          0.340692  0.379788  0.521781  \n",
      "gaze_y_range        0.556784  0.639362  0.885771  \n",
      "gaze_y_median       0.057909  0.089559  0.216245  \n",
      "gaze_speed_mean     0.061470  0.066735  0.082630  \n",
      "gaze_speed_std      0.040688  0.044824  0.056509  \n",
      "gaze_speed_max      0.311362  0.369701  0.594914  \n",
      "gaze_speed_median   0.053352  0.057408  0.073292  \n",
      "gaze_accel_mean     0.000026  0.000043  0.000102  \n",
      "gaze_accel_std      0.051167  0.056961  0.069084  \n",
      "gaze_accel_max      0.247459  0.301947  0.508622  \n",
      "gaze_accel_min     -0.249953 -0.214845 -0.124041  \n",
      "expression_changes  0.237000  0.296000  0.409000  \n",
      "Expression_1_ratio  0.000000  0.000500  0.015000  \n",
      "Expression_2_ratio  0.000000  0.000500  0.201000  \n",
      "Expression_3_ratio  0.000500  0.006500  0.127500  \n",
      "Expression_4_ratio  0.638500  0.859000  0.997500  \n",
      "Expression_5_ratio  0.000000  0.000000  0.000500  \n",
      "Expression_6_ratio  0.317000  0.579000  0.996000  \n",
      "Expression_7_ratio  0.000000  0.000000  0.000000  \n"
     ]
    }
   ],
   "source": [
    "# 使用tqdm显示处理进度，对ASD样本进行特征提取\n",
    "print(\"正在处理ASD样本...\")\n",
    "asd_features = []\n",
    "for file in tqdm(asd_files):\n",
    "    try:\n",
    "        features = extract_features(file)\n",
    "        asd_features.append(features)\n",
    "    except Exception as e:\n",
    "        print(f\"处理文件 {file} 时出错: {e}\")\n",
    "\n",
    "# 对TD样本进行特征提取\n",
    "print(\"\\n正在处理TD样本...\")\n",
    "td_features = []\n",
    "for file in tqdm(td_files):\n",
    "    try:\n",
    "        features = extract_features(file)\n",
    "        td_features.append(features)\n",
    "    except Exception as e:\n",
    "        print(f\"处理文件 {file} 时出错: {e}\")\n",
    "\n",
    "# 将特征列表转换为DataFrame\n",
    "asd_df = pd.DataFrame(asd_features)\n",
    "td_df = pd.DataFrame(td_features)\n",
    "\n",
    "# 显示提取的特征数量\n",
    "print(f\"\\n成功处理 {len(asd_df)} 个ASD样本\")\n",
    "print(f\"成功处理 {len(td_df)} 个TD样本\")\n",
    "\n",
    "# 显示特征列\n",
    "print(f\"\\n提取的特征列: {asd_df.columns.tolist()}\")\n",
    "\n",
    "# 查看ASD样本的特征统计信息\n",
    "print(\"\\nASD样本特征统计信息:\")\n",
    "print(asd_df.describe().T)\n",
    "\n",
    "# 查看TD样本的特征统计信息\n",
    "print(\"\\nTD样本特征统计信息:\")\n",
    "print(td_df.describe().T)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb6017c",
   "metadata": {},
   "source": [
    "## 4. 构建标签与合并数据集\n",
    "\n",
    "接下来，我们将为每个样本添加标签，并将所有数据合并为一个总数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "abd2155a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "合并后的数据集形状: (269, 31)\n",
      "ASD样本数量: 124\n",
      "TD样本数量: 145\n",
      "\n",
      "标签分布:\n",
      "label\n",
      "0    145\n",
      "1    124\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "TD     145\n",
      "ASD    124\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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8N3leVhy/++67OzwuC7849rsT0lPu/PPPV73v7rjjDtUfrvNCaXbS301WApde5zK+1157rUNPWVld/PXXX1e/rX0FdFl4RRZMc1yJXMhvRkREnsETYrfELlmkTBYik3gnfVYl9jqSHq1Dhw5V7z3mmGPUGh5vvvlm+/MSEyUWS2yW/YCTTz5ZrUcycuRIFf+kv6o9psr4JIYtWbJE9W2Vz5c4L3FbCmrsv4estXLqqaeq2BYdHa0u0ndUDBs2DOPGjVP7ELJAmWxP+rTav4uQ7+K48Nc999yj+osPGTKk/TH789InvfO6JfI7dI7d0u/0nHPOUX3T5e8ksbu7xdHkO0rveInv8tr777+/S8/ZX//616o3++9+9zsVq+0LiUufXMfYLWOMj4/v8hlEROQ5PCGejx8/XvUOl7FIP3EZg8QoOcaNiopq/yzpby6PycU+dkdyHCnHzLI+l8QgWe/kqKOO6rBWlzwvfcxlPRF7/BJJSUnqIu8Vo0ePVj3QZf2Qm2++Gfv371d5gtTUVHUs3F28ll7ucozs+DvZn5fv73g8K+uiyO/UOV7L73L55Zerz5DfRXIPnb+nkON5WZtM1kmRfZWnnnqqw1onkkeQ/QxZ+016sUt8tveiLy0t7bBN+Z6yr0I0qNyWvifyEVLpJdVi0p7E3ivcsQ+bVEXb26jImdvOLT9kWpjj2WQ5iyxTu6VPuH0KmVRfS/9UOTMsLUukusvRLbfcol4rVeKOpMpKeq/K1DQhFW9yNv79999XlegyFfykk05SZ4LtZOrYcccd1z71Wcjnyval4k3GI1VejpXcUrEt3+1I7D3RhWzfXvEm08OlAsBegSbTrOXz7NXp0vblxBNPVFVvjpXoJ5xwQo9n6O1V9nbyXSMiItTfqzfT7KRiQCrnpFVO5zPs8vfuTZWAVNUREZHncXfslj6i4eHhKh7JthxJjJXPl97jYtmyZWommVR/y7b/+9//qvYlEteEVMRJ3O7cMkVmmcnrpTp8xIgR7W1h7KTKTqZUH45jT3T7uAsKCtRtqWzrvC6LfH8h07Fl3+Lee+9VPcsdx3a4uOnYq1b2MyTOS4Xexx9/fMSYO3r0aLWfI5/XuTetfH5v4rZcZH+DiIi8gzvjucyolnYp8rrdu3erx3pznNk5HmdlZdleeOGF9vsyHlnDy96DXC6y3cO1DrX3RLe3ipEKdXvvdFlXTGau2yvgpQrf3vpGKuslfkrluszmsleid9cup6d1Q2Sml+QcpLpdvseRvv8ZZ5yh3i/x1j5L3p4fuPzyy3sdr2WmOtFg61rKQUS9VlhYqM4UyxnTZcuWqUoxqQyTai4hFVhypleqyezsVdZi6dKl+L//+z88/fTTahtydvXgwYPqbPJf/vIXdQZaVgu3n9n96aefVCWY45ljOR6Vs+FCxuBIKtSksst+Fltuy1lhe5W5nHGWM8Xy+Xby2s8++6y9SlvOjMvzUpUllWgnnHCCqtKWs8Bbt27t8pvs2LFDbfdIZPtSMSdkpfKPPvpInTmXinT5LYT9DLf8RlJR1131mTzX1pqq/XLhhRd2eZ1Upa1du1ZVqMnK63IGXyr/pKrO8b1SfSAVdnL2W34LqUqwk4p02fbVV1+t7q9Zs0aNVS6y6vrf/va39vtS8edYvU5ERJ7BE2K3VKVJhZVUll177bUdxiexdc6cOZg3b566f9JJJ6nYap+NJRXk8rzMxBISa2TGllSZ2+Xn56u4KrFbvqtUYD/zzDOqgkwq6jrbuHFjr347GbfERyEVfO+8846qUpPfVH4Le9yWeC0V7VKt3h2J9Z1jd+eZdLKfIduQyrszzzxTfW+LxYLk5GQVnx3f+6tf/Qrp6elqP0eq3GfNmqW2IbH+lltuUTP25DUyfnuctu+r/PDDD+2P/f73v+/wdyIiIs/l7nguM5xktpSwH/dJ3JG4JNXW9sdkVpXMFrPHLKmgdiTxzbFyXeLTP/7xj/ZZYXKR+Gf/Xr0h38FepS3HwTKDTfYLZBuO8VqqylesWNF+fNuZ7E84xlvZn+hMtiX5iMWLF6t9G/kMmQVur9B3fL/MnJN4LVX7Utk/atQotQ15vcwAlzHK/s+ECRPaY7PMlhdSlW5/TF7LeE3uwCQ6UT/JP+YyjUumPsuBnxykyXSur776ClOmTEFWVpYKJtLCRBK1Mg1JpiZJOxN7UD/99NNVCxBpZyJBQKYyyQGcBPNzzz1XJWZ//PFH9dpLL71UHahKIHMkwcrehkUOoCXpbSdTpCQoSmCzT/uSA0g7mQInAVp2EOw7FRL0JJjaScCVZPwNN9yg7v/xj39U323nzp1qJ+Ktt95qf63stEybNg333ntvn39P+Q3l93z33XfVOCTo2nc85GDcccfC0aOPPqqCsONFTgJ0JtsbMWJE+/eWJP6WLVvav7tdQUGBmu7WefqZ7FzJ30da5sjBuJBtSAJELvZx2u/bbxMRkefwlNgtJMEt8dk+vdtOPk/arMlBtT2mO8ZuaY/y0ksvqXHL83KwLeNwjFt//vOfVXyTZLK0WpOEe0xMjIqxkox3HI98XzmZLYn4vpA4V1RUpH4PSTB0nh4ucbu7k9/279g5dktblc5k7PLbS7yV30peI5/ZXezurn2a7BtJizY5CS4t2oQ9TtuLBRxjufyGjN1ERJ7Pk+K5cIzB0gpGWof99a9/Vffff/99NU45ru6OHENL0ltiXHFxMT744AN1u/NF2rvZi836Qn4XyQnId5CxdY7Xsn/QE/lMx1gt+YXuyAlu2Z+R31uO4eXvI0nz3sRrOUFwyimnqJMaH3/8sfpMwXhNHmnQa9+JfIQshPGrX/3KZjab2xcp6bxitCzEKVOl5T+17laTfuCBB1QrFFkMxD7dypFMX5bp3rIAmbRd6Tz1Sxx99NG2efPmqfYjMpXtzTff7PIa+wIgvbnIlHA7WWhMprBJGxVpwyLTqu1kQTRZgEWmkTu2c5H2JzLdTBY06a6di0y1k3YyP/zwQ/uUb5lGJ1PtbrrpJvV7fvTRRzaTydTl/bL6eOd2Lvapeo7kN+/czqUzmXLX3SJm0hpn1qxZXV4vrXFefvllddu+uJu0obGT323RokXt92XhGFnolYiIPIenxG4hcUoW2OyOtE/pbdyWy5///OcOU9GlRdqKFSu6xMPTTjutvYWLYzsXeVymejsu8u04nVtitrRvOXjwoHpcXifTqGVxMJnOLmTquUzR7qy7di72KfeOHBd864lME5d9nc7jHDdunGod05mM3x7n5e/i+HvLtHYZi0zNt5NxyniJiMizeUo8t8dJ+yKZcnwox8gSI2Wb9hal0vZFjpGltYpj21TZprxmz549asFPaZMmx6hyDC4xy36xLzYui24erp2LHOPKMakcYwuJ22eddVaHhVfPP/982w033NBlG921c+m8+Kf9ONixnUt3pOWctKJzbAErZLFT+0KvjuR3s8djyTtMmDCh/TlZHFU+U1rUOLasu/nmmw87BiJXYCU6UT/JGdQXXnhBnW3t7iyutB+RyiyZ7iy6q8aSSjFZfEMqrb7++mt1xlqmFdtJdZlMmZKzx9IeRKZ+acefGlkMVFqKyPukelrOLi9cuLDDa4SccZbpabJAqP1MtlSdyaJb9vvynFR3yZlvO6lWk+lf0r7FkVTHySIgsphY55YlcvZe2r5IxbacVRaywKhU1cm1jFWmw8lZdjmrf+KJJ6oKOVmsU6q95feUs+OdFyvpiSxcJmeiHS9ytv9I5DPlbyhVAZ3PjssUs87kb3DllVeq247V/j3pzWuIiMj/YndvyPRmiUdSOWeP0/bFL6VKy/6YPC+vc4zTsvC3TCO3L0But2HDBtUaReJ0Z9LqReKy40LdUm333//+V92WVm4ytV2+m2xDKsuk2k8q22Q6u+hL7JaF0TrHbvuiaD2RVjQPPPCAWjzcvpD5kWK3VM1JKxjB2E1E5Ds8MZ5L6xdZpFRinBzb2o+FJbbIIuDSAk2OoWV2lJ20a5MYKJXcTz75pKqMl5nfMrtbjs/l+FmO92UM//73v7td+FraxUj7M/v+g8R0ickyY3zMmDGqjZtsQ1rB9TVeS2x1jNWOM9Z7IjFZvqe0UXOs0JeWqhLLu4vXkpuYMWNG++91JIzX5C5MohO5gLQ/kVWuJenaecqTBHsJoq+//rpqhyLBWNqMPPzww2p6lkzlkuD529/+Vk1Dlv5ksqr2559/rt4v05Hl4FhID27pKzZp0iR1X94jLUqkl5gjCcrSJ1T6osn0arnYp0bb78tz8tmOq3LLTonsTEjwc9xhkF6kslNhb/Hi+JxMq5IEtWxbkvJysC87NDJ9ToK4BHXpNSd92aVv6bZt29RUu86rcvcU2DvvuPS2J7pjwJUVyiWpL0n7ztPAZEemu8Du6HD96CShIVP3pLVOdzs5RETk37G7NyT+Su9xmaJuj9P2li4yXdr+mDwvr3Ns9yL37b1NHWPmb37zG9VfXQ7MOz8nJ82lj6kcrEuclNifkZGhTlTL2OUEu/SelVYy9nVS5IBcpsz3J3b3pie6IzlhIMlwGd9DDz3U4TkpEpDnBxK7Zc0U+a6S4GDsJiLyboMZz6W4TEgSW/qjS1tTSV7LMaa99Yr9Wo5/5YT2VVdd1f5+6ZUu43CMn7KeiCTQ5QSAHE9L3/frrrtOHU93JieWZZ0xaV0mx9PSEmXfvn2q0O2RRx5RMX/lypXt66r0NV73pie6I0nYn3baaer43752i+NxthhIvJbe97JmmZz0YLwmd2ATISInkANOe8CR/l/Sd00OgCUY2x+Xf/BlEU85iy3VVnLmW4KcBEl5vZzVlaAkZ23lYq80lyS5BHZZKEUqzSUpLWfMly9frg5CHXuYyhlceb0k1yXAS/CVyjUZi+NZentwkuelGtyR9E6X57KzsztUmcsZa/tCLHfccYcaswRgqUyT7+Z45l92DOwH8JKcnz9/vtoRkQVCHMkOjOxsOAZ1kZubq862d2bv2S7BXBYfkZ2W7r6DPCePS4Jegqs9wK5evVpV2UnQlSSAfSEYOSMuB+XyufIe+f36Gtjt/x+QkxWyOKk40naIiMi/YndvyAGo9EeVGGzvAyrkoN5e4dU57tnjs5ys7vy4/TlJHEiMX7dunUqKywG/42wsed5eMSb7EPLZEu8loeDoX//6l0rkd65GkxgqSfaeYrf8xvZKwO6+g/w9ZJ9Fes5KQkAO8OUxGascMMtJatnnsR98yyw7WbBM9ockWeGY0O9N7LZXscnfWgoIpEBA9qPkZAMREXkPd8VziaOSrJfCMlmTS9bNOvvss9W6JXIiWvqOS3W6JPX37t2ritAkvkufdflMOektxW9ygtuRvE/inYz3jTfeULFavkt3JNE/efJkNY7Oa4jJ7Gyp2HfMA8hvJUl2qZjvKV5LfJWYK+S19oW4HfdF5PvIiW2Jm/b3SoW9nJCWz5PZavb9BInT8vvId5Jj5c7H/n2J17JtWRNNZtvZZ8IRDSqXNIkh8jPSK+3LL79Uvb7t/cs2bNignpN+ZJMmTWrvWyp9zqRfdnfktb/4xS9sN954Y5e+bH//+99Vn/AzzzyzvcfZkbz++uuqL6p8pmNPtcNdZPzSr61znzPpgT5lypQunyG9wKV/6JH6mB6J9IY75ZRTbDNmzFD90U8//fQur5F+p6NGjbLdcccdapwRERE9fg95Tr63vVer9IOTnuzy/mXLlnXZtvRxlT5wDz/8cJfebb0xbdo021//+ld1W3rzERGRZ/PU2H3NNdeo3qeyrkZvY7d8xrBhw7psS3qd//a3v+3wWF5eni0rK8v2y1/+0rZv3z7bQEgf2ZkzZ6repfI7Oa4NYifrjFx//fVqH0J6oR7ue8jfRPqsS09X8d1336l9AonRubm5XdYqkX6rsh/yv//9r89jl/fLmJcvX67uM3YTEXknd8TzpqYmdbz60ksvtb9G+prL+mAXX3yx+kw59kxISLBFR0erY1P7sevQoUPVNtasWaPG9NNPP6ntSUyWvuUSt+U1cpHPk+N52Z70AJfe6vI9t2zZ0qff6IsvvlDxWo6F5TPfeuutLq/JyMhQcTwuLu6I8Vqel30VWV9EyPakl/vll19uKy4u7rDd/fv3q/fMnj1brdfSV9u2bVNjlv0XOU537ClPNNh08j+Dm7Yn8j1ytljO9MoZ0bPOOkv1K7OflXUmOSsrlWmOvcV8iZxll8o0+R2lF7uc9Xcmqe6TirrOfdydQf7eUnkg/VqJiMjzMXYPnFSnSSWdXCSGSwyUqfHOJPsFzt4fEFJBJ7PlpMe79JEnIiLv5K54Lj3PHWeM9ZXMrpZZYDKz+84771QzvaT6XHqDS5sWqRaXzyopKVEV9FLRLe3Hdu7ciW+++QbHHHNMrz9LKspl2xJPzz33XFUxPpCxD2a8lhY40r5Wer+7YvtEfcEkOpGTyX9SvnigTERE5KsYu4mIiLyft8Zze9/0zi1YuzPQ5D0R9R+T6EREREREREREREREPdBW9yEiIiIiIiIiIiIioi6YRCciIiIiIiIiIiIi6gGT6EREREREREREREREPTDCz1mtVhQWFiI8PNwrF6AgIiLfJcuW1NbWIiUlBXo9z3vbMXYTEZGnYuzuHmM3ERF5e+z2+yS6BPL09HR3D4OIiKhH+fn5SEtLc/cwPAZjNxEReTrG7o4Yu4mIyNtjt98n0eVMuP2HioiIcPdwiIiI2tXU1KgDTnusIg1jNxEReSrG7u4xdhMRkbfHbr9PotunkkkgZzAnIiJPxGnPHTF2ExGRp2Ps7oixm4iIvD12s0kbEREREREREREREVEPmEQnIiIiIiIiIiIiIuqB37dzISKiwWWxWNDS0uLuYXiEgIAAGAwGdw+DiIjosBi7D2HsJiIib8DY7fzYzSQ6ERENCpvNhoMHD6KqqsrdQ/EoUVFRSEpKYu9UIiLyOIzd3WPsJiIiT8XY7brYzSQ6ERENCnsgT0hIQEhIiN8feMrOTUNDA0pKStT95ORkdw+JiIioA8bujhi7iYjI0zF2uy52M4lORESDMpXMHshjY2PdPRyPERwcrK4loMtvw+nhRETkKRi7u8fYTUREnoqx27WxmwuLEhGRy9l7scmZcOrI/puwXx0REXkSxu6eMXYTEZEnYux2bexmEp2IiAaNv08l6w5/EyIi8mSMU13xNyEiIk/GOOWa34RJdCIiIiIiIiIiIiKiHjCJTkRERERERERERETUAybRiYiIiIiIiIiIiIh6wCQ6EREREREREREREVEPmEQnIiIiIiIiIiIiIvKkJPrixYsxbNgwGI1GTJw4ETk5OerxLVu2YOrUqYiOjsaCBQtgs9l6tb333nsPQ4YMQUpKCt566y0Xj56IiDzBpk2bMG7cOMTExOCOO+7A6NGj8dRTT+HEE0/Eyy+/jMcee0zFhk8//bT9PcuXL1dxR+LMvHnzUFVVpR6X18v77Pbt29e+evdf/vIXnHnmmTjhhBMQGRmJyy67DDU1NW74xkRERN6NsZuIiMi7MHa7MYmem5uLq6++Gg8++CAKCgowcuRIXHPNNWhubsY555yDKVOmYM2aNdi2bZv6cY9EEu+XX3457rnnHnzxxRe49957sWPHjkH5LkRE5D433HAD5s6di2+++QYvvPCCusyfP18995///Adffvmlup4+fbp6LD8/H2eddRZuvvlmrF27FnV1dbjqqqt69Vmff/45fv3rX6v4JIFeYg4RERH1DWM3ERGRd2HsPsSIQSZV55JAv+SSS9T9G2+8EXPmzMFnn32G6upqdQYjJCQECxcuVD+4JNwP5/nnn8dJJ52kEvHilltuwWuvvYYHHnig29dLsl4udp52VoOot/Ly8lBWVuaUbcXFxSEjI8Mp2yIaLBs2bFAnW+Vk7NixY9V/E8cdd5x6TgL1d999h8DAwPbXv/766zj22GNx7bXXqvv/+te/kJaWhoMHDx7xs2S7V1xxhbr9hz/8QZ2Bf+KJJ1z23YiIyPn7O4L7PO7F2E1E/owxjbwRY7cbk+hnn312h/tSNZ6VlYWNGzdixowZKoEuxo8fr6rRj0TeJ+X+dtOmTcNf//rXHl+/aNEi3H///QP6DkTuJv9ojc7ORmNDg1O2FxwSgu05OQzA5FVGjBiBlStXqp3HXbt2YcyYMR3OljsGcvsZcWklZpeamoqgoCD131NnDZ3+20pPT+/wvuLiYid/GyIi6kz+fc7OHo2GhkanbTMkJBg5Odu5z+MmjN1E5K8Y08hbMXa7MYnuyGw249FHH8Wdd96J3bt3Y+jQoe3PSU8cg8GAyspK1UOnJ1JJ7vi+iIgIFBYW9vj6u+66S32e4/sd/0hE3kDOXksC/fI/PILEjOED2lZxXi7eeGiB2iaDL3kLWTNDgvdtt92mznDLLKQJEya0Px8aGtrlPfL/72XLlrXfl1ghM5Okf5vMkrJare3PybQzRzKVzHGnICkpyQXfioiIHMm+iSQbXv/TJcjOiB/w9nLySjF/4Tvc53ETxm4i8meMaeSNGLs9KIl+3333qR9cWrHcfffd6syEI5PJpM5KHC6JLouTOr7P/p6eyGs7fw6Rt5IEelrWWHcPg2jQySym77//HitWrFCLjvTmZKisnyGtvp577jnMnj0bv/nNb3D++ecjMTFRneWW2U9yYlUC/MMPP9zhvatWrcIrr7yipqU99NBDuPDCC1347YiIyJEkGyaPTHX3MGiAGLuJiBjTyLswdrt5YVE7OSvx9NNP480330RAQIBa5bW0tLTDa2pra7tMC+is8/t68x4iIvJu0o8tISFBrdwts5GCg4PVVLLDkYC/ZMkSFXsmTZqk2oe99NJL6rmTTz4Zp512Go466ii1Tsef/vSnDu+Vha9lDY7Jkydj+PDh6iQwERER9R5jNxERkXdh7PaASvS9e/eqlV3lB7X30pk6dao6S+H4GjkrIUnyw5H3SW8eWb1VrF+/Xp3ZICIi3yVBWIL5//73PxWUN23apFYAlzPZ3377bY/vk+AvC6N0ptfr1UldRxKn7CQWyWcRERFR/zB2ExEReRfGbjdXojc2NqrFRc877zxccMEFaiVXucycOVOV89vPTixcuFCV/UtfdFFVVQWLxdJle1La//bbb2Pz5s1qO08++SROP/30wf5aREQ0iE466SS0trZi3LhxSE5OxnXXXYcHH3xQrYtBREREnoexm4iIyLswdru5En3p0qWq/41cOleeS8m+nIFYsGCBOjvheFZD+qJLlfnEiRM7bE8a2t9+++04+uijVT/0rKws3HTTTYP6nYiIaHDJat9ff/31oHzWX/7yl0H5HCIiIl/G2E1ERORdGLvdnESXCnRZ3bU7mZmZyM3NVauzzpgxA7Gxse3P9fQe8be//U01ri8oKFBTBtgTnYiIiIiIiIiIiIi8tif64SQlJanm8n0lvdXt/dWJiIiIiIiIiIiIiLyyJzoRERERERERERERkbdgEp2IiIiIiIiIiIiIyFvauRARkX/Jy8tDWVnZoH1eXFwcMjIyBu3ziIiIfA1jNxERkXdh7B44JtGJiMitgXx0djYaGxoG7TODQ0KwPSfH5wI6ERHRYGDsJiIi8i6M3c7BJDoREbmNnAmXQH75Hx5BYsZwl39ecV4u3nhogfrcvgTzr7/+GqeeeiqKioqQmJioHlu1ahVuvvlm7Ny5E9OnT8crr7yC1NRU/OUvf8H9998Po9GIzMxMXHPNNfj9738PnU7nwm9GREQ0OBi7iYiIvAtjt3MwiU5ERG4ngTwtayw81dKlS2Gz2fDll19i/vz5aGhowHnnnYcHH3wQs2fPxm233YZbb70VH3zwgXr9WWedhWeffVbtBMjjLS0tuPvuu939NYiIiJyGsZuIiMi7MHYPDBcWJSIiOgIJ4ieeeKK6Fjk5OaiqqsLVV1+N9PR03HvvvbBYLO2vDwgIUGfHr7jiCjzyyCN48sknYbVa3fgNiIiI/AtjNxERkXf50sNjN5PoREREh1FaWoqNGzfit7/9bXswlwCu1+vxwAMPoLW1FZMmTcLixYu7ff/pp5+utrFv375BHjkREZF/YuwmIiLyLqVeELuZRCciIjqMr776CiNHjlTTx8rLy7F582YkJCTgtddew9///neMGDFC3e5JcnKyui4pKRnEURMREfkvxm4iIiLv8pUXxG4m0YmIiI7Ql23GjBkwmUzqzLf9rPhFF12E/fv346qrrsJ1112HBQsWdPt++8Im0tuNiIiIXI+xm4iIyLss9YLYzYVFiYiIDkOCt6wq/uGHH6qFTaKionDZZZehsbERw4cPV6uCS9+2k08+WS1m0llxcbG6tq8uTkRERK7F2E1ERORdvvSC2M1KdCIioh5s27YNBQUF+O6777BhwwY8//zzWL58uZpGds0117S/btasWTAajWrRk+7OqCclJWHo0KGDPHoiIiL/w9hNRETkXbZ5SexmJToREbldcV6uR36OBGLpvTZ9+nR1PzY2Fr/+9a/VFLMff/wRb731lgrk//nPf1QPttGjR6vXtbS0tO8E/O53v1OriNunlxEREfkCxm7fJgmKHTt2qP600dHR7h4OERE5AWP3wDCJTkREbhMXF4fgkBC88VD3fc1cQT5PPre3U8pkuphdeHg4pk2bhsLCQrz00ku477771G37KuGBgYHqdZ9++ikyMzPVjsCiRYtw/fXXu+z7EBERDSbGbt/37rvv4tprr0V6ejr27NmDl19+GRdffDG2bNmCq6++Grt371aVgQ8//LBfn2ggIvIWjN3OwSQ6ERG5TUZGBrbn5KjeZ4NFArl8bm8sWbKky2MrVqxovz1v3rwuz0uvNrkQERH5IsZu31ZdXY2bbrpJTaMfP368SqDLIm7nnnsuzjnnHJx++ul4++23cdttt6nnJKlORESejbHbOZhEJyIit5LA2tvgSkRERO7H2O27ampq8I9//EMl0MXkyZNRXl6Ozz77TCXYH3vsMYSEhGDhwoW4+eabe0yiNzc3q4vjdomIyH0YuweOC4sSERERERERkWrhcvnll7f3mn388cdxwQUXYOPGjZgxY4ZKoAtJsstCcD2RafWRkZHtF9kuERGRN2MSnYiIiIiIiIjaSdI8KSkJn3/+OZ588klVST506ND256UXusFgQGVlZbfvv+uuu1Tluv2Sn58/iKMn8hKtzUBJDrD9E2DrB0Du10B5LmCzuXtkRNQNtnMhIiIiIiIionZSab506VLccccdahHR4cOHIygoqMNrTCYTGhoaEB0d3eX98trOryciB5I83/UF0NrU8fEDPwPRmcCIU4GQWHeNjoi6wUp0IiIiIiIiIupQaT5lyhS88sor+OCDDxATE4PS0tIOr6mtrUVgYKDbxkjklWxWrfI8Z7GWQDdFAenTgeGzgeSJgM4AVO4D1r8G1Hf8b46I3ItJdCIiIiIiIiLCd999hwULFrTflyS5JNSzs7OxcuXK9sf37t2rFg6V5DoR9Y5eB2RWrQCKt8ipKmDIccDUa4FhJwFpRwMjzwCmXgOEJ2sJ9k3/BZqq3D1sImrDdi5ERORWeXl5KCsrG7TPi4uL46rkREREA8DY7btGjhyJZ599FllZWTjzzDNx991347TTTsNZZ52l2rq89NJLuPrqq7Fw4ULMnj1b9UUnot751xwTYpr2ATo9MOYCIC6r64uCo4GjLgE2vAE0lAFb3gcmXwXo+d8aDQxj98AxiU5ERG4N5NnZo9HQ0DhonxkSEoycnO0+F9CJiIgGA2O3b0tOTsZ7772H3/zmN/jd736H008/Ha+++iqMRiOef/55zJ07V1Wq6/V6fPvtt+4eLpHXiMn7HNdNCYQNOuiyz+0+gW4XEAyMvxRY86LW0qVgjdbyhaifGLudg0l0IiJyGzkTLoH89T9dguyMeJd/Xk5eKeYvfEd9bm+C+csvv6yqrTqzV2HJAWRqaiouu+wyPPDAA+wLSkREPo+x2/edeuqp2Lp1a5fHzz33XOTm5mLt2rWYMWMGYmO56CFRr5TuRPrmJ9TNorAJSIkffeT3BIUDw08CdnwK7PsBkPeYIl0/VvJJjN3OwSQ6ERG5nQTyySNT4WnmzZuH888/H5s3b8asWbNQWVmpHpcKrXHjxuGrr77CqlWrcNNNN6GiokJVaBEREfkDxm7/lJSUhDlz5rh7GETew2oBPrgWBksTvt7TiuhjxyKlt+9NPAo4uBmozgdylwFjL3DtWMnnMXYPDBcWJSIi6oGc4Y6KikJ4eLi6L7flIlOapQdoYmIizjvvPHWGXKY6S0AnIiIi92HsJiKPIi1Zijag1RiK+R82av3Qe0unA7JO026X7QAayl02TCJ3CvSS2M0kOhER0QCdfPLJ6nrDhg3uHgoRERH1AmM3EblcfRmw7P/UzaLsX+Ngna3v2wiNB2Lb+qfnr3byAIm8y8lujt1MohMREQ2QnCGX1cdLSkrcPRQiIiLqBcZuInK5r/8KNFWrtiylQ87t/3bsi4oWbwGa65w2PCJv4+7YzSQ6ERGRE+h0Oths/aguISIiIrdg7CYilynPBda/rt0+6xFAb+j/tiLTgIg0wGYBCtY4bYhE3kjnxtjNJDoREdEAWSwWtfK49GojIiIiz8fYTUQutfzvWtJ7xGxgyDED3176VO26eDNgsw58e0ReyOLm2G10y6cSeYm8vDz1H6izyLSTjIwMp22PiDzDd999p64nT57s7qF4nMWLF+OOO+5Q/57KyupvvfUWsrOzcdttt+Gpp55qf93w4cOxe/dut46ViIj8B2M3Ebm0Cn3T29rtE//knG3GjAACggFzPVC5D0CQc7ZL5EW+c3PsZhKdqAeS8MnOHo2GhkanbTMkJBg5OduZSCfqJCev1Os+R86CFxcX4+eff8ZNN92EG2+8Ua0gTofk5ubi6quvxr///W+ccMIJuPXWW3HNNddgxYoVWLNmDZYsWYJjjz1WvVZWXSciIu/B2E1EdLgqdCuQdTqQNsU525R2MPHZQOE6rTe6wUnbJb/C2D0wTKIT9UAq0CWB/vqfLkF2RrxT/hGZv/AdtV0m0YkOzc6Qk0vy38Zgkc+Tzx2oLVu2ICUlBUOGDMH111+PP/7xj04Zny/JycnBgw8+iEsuuUTdlx2eOXPmoLW1FVu3bsWsWbMQFhbm7mESEVEfMHYTER1GbTGw+V3t9gm/d+62E8dpSfSyndDHj3futsmnMXY7B5PoREcgCfTJI1PdPQwinyQnlGR2hjPbJrmirdLEiRM7LF5y1VVXqQsd3tlnn93h/o4dO5CVlYXNmzfDarWq37WgoEBVqT/77LM9/l2am5vVxa6mpsblYyciou4xdhMRHcaaFwFrC5A2FUg72rnbDk8GgmOAxgpENeU5d9vk0xi7nYNJdCIicisJrJyd4fvMZjMeffRR3Hnnndi2bRtGjRqleqLLzpX0TL/uuuvw+eefd/veRYsW4f777x/0MRMRUfcYu4mIutHarCXRxfQbnL99nQ5IHAvs+x7RTdIXnaj3GLsHjkl0IiIicrn77rsPoaGhqid6QEAALr/88vbnnnnmGQwdOlRVmEdERHR571133aWS73byuvT09EEbOxERERHREW39EKgvAcJTgDHnueYz4kapJHp480GEBLjmI4ioe0yiExERkUstW7YMTz/9NFatWqUS6J0lJCSo9i5FRUXdJtGDgoLUhYiIiIjIY9mr0Kf+GjC4KMMdEguYoqBvqsKpw5jSIxpMeriJ9OGRqrN9+7QpKC+//DJ0Ol2Xizx+JOPHj+/wHqlyIyIizyOJUvKv32Tv3r2YO3euSqKPGTNGPbZgwQK8+eab7a9ZuXIl9Ho9q8uJiDyQr8ep/uBvQkRdlO0G8n8CdAZg0nzXfY60dIkdoW6eM4pJdOoe45RrfhOjuxLostiYPYEu5s2bh/PPP7/9fl1dHSZNmoSZM2cedlsNDQ3Izc1FSUlJe3Ubq9WIiDxLYGCgSpIWFhYiPj5e3ZeTnv5MFkyRPuGlpaXqt5HfxNc0NjaqeH/eeefhggsuULHdfvL77rvvRmJiIiwWC2699VZcccUVCAkJcfeQiYioDWO3f8ZuIuqnjW0FIiNmA+FJrv0sSaIXrMHZWUYU2JgspUMYu10bu92SRL/ssstU0vynn35qf0y+hOMXkf6ocsA9fPjww25r/fr16mBc/s9BRESeSYKVzD6Sdh0S0OkQSRzLAi/yG/mapUuXqkVE5fLcc891qE6/9NJLceGFF8JgMGD+/PlYuHChW8dKREQdMXb7Z+wmon6wWoCNb2u3J85z/edFpsOiC0BiWAsqK7cDONr1n0legbHbtbHbLUl0OZCWP+rtt9/e7fNNTU144oknOiTZe7J69WocOHBAJdFbWlrUlPF//OMfPVajNzc3q4vj4mREROR6cqJUglZra6uqPiaoBLLRaPTZ6gCpQJcz/91ZtGiRuhARkedi7Pa/2E1EHeXl5aluCocTXvIzsmoK0BoQjs0NSbCtW9flNTk5Oc4blN6AmqAURDftR+vWj7EuRmuZOBBxcXHq33vyfozdrovdbkmiSwL9cKRP6vTp05GZmXnEbe3YsQPHH388/vKXv6CqqgqXX345Hn/8cfzxj3/s9vVywH7//ff3e+xERNR/ErSk9VZ3i0sSERGR52HsJiJ/TqBnZ49GQ0PjYV/36vkmZE0IxH9+LMctdx9z2NfWtrU3HPDYWmMQjf2o3fQJjv3NOwPeXnBICLbn5DCR7iMYu13DI1ch+Pe//62S4r19raN7770XTz75ZI9J9Lvuugt33nlnh0p0LmRGRERERERERER2UoEuCfTX/3QJsjO6byGss1kwvvhdwNaC2Wecg7XnJnT7uk9X78Q9L36pOi84w97GCEwIAKalBeCup95Asz6439sqzsvFGw8tUN+XSXQiL0qi7969W11OPfXUfr0/ISEBBQUFPT4vbV648CgRERERERERER2JJNAnj0zt/sny3cDBFiAwDKPGTpQS4G5flpNX6tQx1SMYuRVWDI/RY2p8M/bFsC86kat53Eoo77zzDs4+++xeTzk45phjkJ+f335/5cqVGDJkiAtHSEREREREREREfq90h3YdP6rHBLqrfLOvVV2nV/88qJ9L5K88Lon++eef48QTT+zyuPQ7764h/tixY3H99derRUhfeeUVPProo7jxxhsHabREREREREREROR3rBagfKd2O27UoH/8sr32JPraQf9sIn/kUUn0xsZGlQw/9thjuzwXHR2NzZs3d3n873//u2rPctJJJ+G+++7DI488giuvvHKQRkxERERERERERH6naj/Q2gwEhgKRaYP+8d/s0wpNE+p3IKi1ZtA/n8jfuLUnus1m63A/ODgYzc3NvXqtXVRUFD788EOXjI+IiIiIiIiIiKiLMnsV+khAN/g1qgfrbChEPFJQirTqdciN7drVgYh8tBKdiIiIiIiIiIjIo0mhZ0Wudjs2y23DyMEIdZ3Gli5ELsckOhERERERERERUW/VlwLNtYDeCESmu20Yu3RD1XVybdf2x0TkXEyiExERERERERER9Za9Cj1qCGAIcNswdmNIe190g7X79shE5BxMohMREREREREREfVWeyuX4W4dRiliUB8QA4OtFYl12906FiJfxyQ6ERERERERERFRb7Q0AdUF2u2YYe4di06HovDx6mZy7Sb3joXIxzGJTkRERERERERE1BuVe2VlUSAkDjBFuXs0KAo/Sl2zLzqRazGJTkRERERERERE1OskulSha4t6ekoSPaVmE2CzuXs4RD6LSXQiIiIiIiIiIqLeqNqvXUdlwhMcDMuGRWdAaEs5IpqL3D0cIp/FJDoREREREREREdGRNFUBTdXSjByITIMnsBhMKA0dpW6zpQuR6zCJTkREREREREREdCRVedp1RApgDIKnsLd0Sard6u6hEPksJtGJiIiIiIiIiIiOpNLeymUIPElJ2Gh1nVC/3d1DIfJZTKITEREREREREREdjizaaa9Ej8qAJykObUui1+0AbFZ3D4fIJzGJTkREREREREREdDiNlYC5FtAZgIhUeJKKkEy06IMQaG1AdGNbop+InIpJdCIiIiIiIiIiosOp2n+oH7ohAJ7EpjOiNHSkup1Yl+Pu4RD5JCbRiYiIiIiIiIiIDqf6gEe2crErDstW1+yLTuQaTKITEREREREREREdTk1bEj0iDZ6opK0vOivRiVyDSXQiIiIiIiIiIqKeNNcBTdUAdFo7Fw+uRI+v38nFRYlcgEl0IiIiIiIiIiKiI1Whh8YDxiB4IvviokGWei4uSuQCTKITEREREREREREdqR96pGe2cum8uCj7ohM5H5PoREREREREREREPakp0K4jUuHJ7En0+Ppd7h4Kkc9hEp2IiIiIiIiIiKgbOlsrUFes3Yn0jiR6HJPoRE5ndP4miehwcnJyPGIbRERERERERHR4oeZybaHOwDAgKBKerCxkhLqOb9jt7qEQ+Rwm0YkGSVFFrazjjfnz5zttm3V1dU7bFhERERERERF1FNpSeqiVi06O6j1XWchwdR1mLoWppQpNAVHuHhKRz2ASnWiQVNU1wQbgnzedhmPGZw1oW5+u3ol7XvwSTU1NThsfEREREREREXUU0lKu3QhPhqdrMYaiypSKqKYCxNXvxoGoo909JCKfwSQ60SAbkRKNySMH1kctJ6/tTDgRERERERERuYw3JdFFWUiWSqLHN+xiEp3IibiwKBERERERERERUSfxIToEWeq1O+FJ8AalodrMd6lEJyLnYRKdiIiIiIiIiJTFixdj2LBhMBqNmDhxInJyctTjt912G3Q6XftlxAhtAUMiXzY11aDdCIkFjEHwBmWhbYuL1u9y91CIfArbuRARERER+bm8vDyUlZU5ZVtxcXHIyMhwyraIaHDl5ubi6quvxr///W+ccMIJuPXWW3HNNddgxYoVWLNmDZYsWYJjjz1WvdZgaEsuEvmwqSkGr6pCF6UhWiV6bOMe6GytsOmY+iNyBv6XRERERETk5wn07OzRaGhodMr2QkKCkZOznYl0Ii8kVecPPvggLrnkEnX/xhtvxJw5c9Da2oqtW7di1qxZCAsLc/cwiQbNtNS2Bg7hKfAW1aZUtOhNCLA2IarxACpDMt09JCKfwCQ60UDYrEBdCVBTADTXAq1NgNEEBIUBEWlAWCKg07l7lEREREQ9kgp0SaC//qdLkJ0RP+DFz+cvfEdtk0l0Iu9z9tlnd7i/Y8cOZGVlYfPmzbBaraq9S0FBgapSf/bZZ3v877y5uVld7Gpqalw+diKns9kcKtE9c1HRyqoqFBUVdXm8MCADQ5p3wljwM4rCDt+Gxlkz0Yh8HZPoRP3RWAkUrgeKNwMth6naCggFkicAqVwRm4iIiDybJNAnj0yFp9FbW4CSbUD1AcBcB1gtQGg8EJECxI4AdFzmicgVzGYzHn30Udx5553Ytm0bRo0ahaeeekq1bLrjjjtw3XXX4fPPP+/2vYsWLcL9998/6GMmcqbAxmLEh+phgw66sAR4krJqbbHTb5Ytw/LVG7o8Py67EVekAgWrPsSze7YddluWugp13V0ynogOYRKdqC/M9cC+74GijXJaWnvMEAhEpgPBUVoVulSjN1YBVXlASz2Q9yNwYDXG6IfB6IJjvBaLFfkVDSioakRzqxVWqw3RoYFIjjQhJSoYelbCExERkRcymKvx+OlBOKrkXaDY0vHJilztOjgGyDweiM/m7D8iJ7vvvvsQGhqqeqIHBATg8ssvb3/umWeewdChQ1WFeURERJf33nXXXSr5bievS09PH7SxEzlDSNUOdd1ojEaI3rPSZ7WNZnV93Ng0jD9qbJfno1tbgNZ8nDkiAOVjph52W9t37MLHG4GqqiqXjZfIF3jWvwJEnspm06rOd38FWLRgheihQOoUIGZY9xVQUiVVvgvI/wmoLcIkw078dE0o6m3aGeOB0gdHINccgRXL96DV2pbQ7yQyOABThkRjTHIEDHoeWBIREZGX7HdteANjv/4DJswIAmwWwBQFxGVp19AB9SVA6XagsQLI+R9QuR/IOg3Qc6FDImdYtmwZnn76aaxatUol0DtLSEhQ7V2kcrW7JHpQUJC6EHmz4Ord6rohIAYh8EyRIYFIju3632BLUyZQDqTqy7t93lFxmMmFIyTyHUyiEx2B3moGtn0IlO08tCr38FO06vPDvtEAxI8G4kYBxVvQvH0pJicDLa0rgcoUILr/i3tssA5F6vXP40CrhHIbIkxGZMSGINyk7eCW1TYjr6IB1Y0tWLa9BDlFNThjbBIigrvuABMRERF5jOY64JM7gM3vqAOVTcUWBGefhqxxR3etNB92kprth/0/Agc3agn1oy4BDNzfIRqIvXv3Yu7cuSqJPmbMGPXYggULMGnSJMybN0/dX7lyJfR6PavLyaeFVO9qT6J7m/KAJHUd1VoKva0VVh3Tf0QDxf+KiA5jZKweo8s+Ayw1WrV55kwgfXrfem/KAV/SUfhkSz1SDn6FY2Q/c9N/gexzgARtp7S3mq063LMtFe/axkMfBITpzDhlfCaGxIZA1+nAUtq8bCmoxqo9FSiqbsIbq/NwzvhkpEV76jl0IiIi8mu1xcAbFwEHNwE6AwpGXYVJf30cP/8rpftWLcYgbd8sPEWrRq/OB7Z/DIy5gK1diPqpsbFRLS563nnn4YILLkBdXZ16fPz48bj77ruRmJgIi8WCW2+9FVdccQVCQnhsQb4ruEZrHdYYEA1vU6ePRLPOhCBbE6JbS1Ee4JkLoxJ5E67CQ9SDsLINqv2KSRLoQeHApF8CGcf0e/GqJphw0isNKNSlaP3Ucz7WFsnqpUaLDteuy8Q7BTHQwYaqH97AZFMZMuNCuyTQRYBBj0kZ0Zg3PQNJESaYW61YvKFQ9U8nIiIi8ijlucALs7UEekgccNUSFI+cjx461nUUOxw46iKVeFczB/d+NwgDJvJNS5cuVYuIPvfccwgPD2+/zJw5E5deeikuvPBCVaV+xhln4J///Ke7h0vkOnWlCGwqg9VmUz3RvY5Oh3JjoroZ23LQ3aMh8glMohN1J+djjFj1e0SZdKgLiAcmXwWED/zMbbMF2GwYryrT2xPp5VqftSMl0K9aOxTLy8MRbLDiSv3XqF7xVq+KrKQv+oWTU5EZG6J6py/eWIjCqsYBfxciIiIip6jYA7x8trYou6w1c82XwJBj+rYNabM36iztdv4qoHKfS4ZK5OukAt1ms3W5ZGZmYtGiRWrhwfLycjzxxBNq0VEinyUndQHsKrfCqvfONmEVbS1dYluZRCdyBibRiTrbvgR450rorS34MKcFu2JnA4FO3EGUzPfIs4DEcVoifdtioK64x5dbbMBtmzLwU2UYwo0WvDZlD7J0hX36SKNBjznjk1Ui3WK1YcnmItQ1tTrhyxARERENgCTOXzkXqC3U1pL51RdaIr0/EscCKZO12zs/O7QYPBERUV8d3KyuNhy0wluVG7UkekxLz/kGIuo9JtGJHO35Dnj3KsBmQXnaabjo3UbYXLEAh0qknwlEDQGsLcDmdwGz1m+ws//bnoIvSyIRqLfixcl7cXR0/9qxGPV6nDkuGbFhgWgwW/DJ5kK0Wrx3h4CIiIi8XGMV8MbFWi/z2Czgiv8BYQkD2+bQE4CgCKCpGti73FkjJSIiP61EX3/QAm9lX1yUlehEXp5ELysrw9ChQ7Fv36Gplrfddpvq7Wy/jBgxolfb+u6775CdnY24uDg89thjLhw1+bQDa4C35mpVS9nnYP/E3/euD2d/6Q3A2AuAkFgtgS6tXWwdk9rvF0Th5bw4dfvRcfmY2s8Eul2gUY9zxqfAZNSjuKYZq/ZWDGh7RERERP1iaQHevRIo3a61zLviIyBc6906ILLYqBQqiIK1QH3ZwLdJRER+XInuxUn0tp7oUa2l0Ns4E53IK5PokkCXFb8dE+hizZo1WLJkCSorK9Vl/fr1R9xWaWkpzj33XLW4ycqVK/HGG2/gm2++ceHoyScVbwNevxBoqQeGnQRc+IKW5HY1owkYcwEgPdaq9gP7V7Q/tasuCHfnpKnbtw8vxjnJ1U75SOmRPnuMFkzX7a9EeXMvGqsTEREROYvNBnxyB7DnWyAgFJj3XyBS2+dxipihWmW7tM3bx2p0IiLqI3M9ULZL3Vzvxe1c6gxRaNYFwQAroltL3T0cIq/ngj4VR3bZZZdh3rx5+Omnn9ofa21txdatWzFr1iyEhYX1eluSNE9JScE999yjqtfvvfdevPDCCzjppJO6fX1zc7O62NXU1Azw25Az5OXlqZMrziAzEjIyMvq2mNVrFwBNVUDaVODS17UqJhfZVViB+J0FHR6LjpiOoVU/wLZ/BXbVhqI8IBm/LzwWjRY9xpvKcLxlHdbtPPT6vQcrBzSG4fFhGJ0Uju0Ha7Gm3AidMXBA2yMiIiLqtR8eB9a/Buj0wEUvAskTDvvynLy+H/ibdKORjV3Qle3E9i0b0RAY16/tEBGRnxbZwYaWoBiU1HtxzkinQ4UxCckt+xHTWqzyDETkZUn05557TrVyuf3229sf27x5M6xWKyZOnIiCggKccMIJePbZZ4+YDN24caNKmEsCXUybNg1//OMfe3y9rCh+//33O/HbkDMS6NnZo9HQ0OiU7YWEBCMnZ3vvEuk1RcCr5wN1B4GEscDl7wJBvT+J0xdl1fXq+tZnvgQgl46eP9eEX08KREDu5zh/68UIPjoclrpKLHnqN/i4oarbbTY09L+9ywkj45Ff2YC6Zgsipl/Y7+0QERER9dr2T4Gv2/bFz3gIGHVGjy8tKipS1/MXvtOvj3r5PBOunBiIvNUf4/TXG7psl4iIqFslW9VVQ8RwAB07KHibSmO8SqJHt5QAwe4eDZF3c0sSXRLonW3btg2jRo3CU089pSqJ77jjDlx33XX4/PPPD7stqSQfM2ZM+/2IiAgUFhb2+Pq77roLd955Z4f3p6en9/u70MBJBbok0F//0yXIzogf0LakwkgOtGSbR0yiN1QAr52vtVGJHgr88kMgOBquUttoVtezz78Y48dmd3k+F82osL2OisgkhBz9C5mAjOMjSpFyx21dXrt69c/44cvP0GzWttkfpgADZmXF47MtBxEx/SKU1LNHGhEREblQ5T7goxu029OuA6Zfd9iXV1VpRQRzLpmPUVnD+vxx+aiGxfYKThtuxKN/vgHLdtVhyTuvt2+XiIio50p0oClCYs/X8GYVAQlAI9jOhchbk+jdufzyy9XF7plnnlHJdklyS2K8J0ajEUFBh1pvmEymw1bnymsdX0+eQxLok0emDs6HNddqPdDVYlYpwBWLnbOYVS/ExMUjbciQbp/7oulKPFeYCRt0mBJcjGkpkdLFvMvrduzKdcpYshLC8HOQFWUIwqsba3HGTKdsloiIiKij1mbg3auApmqtfd5pf+v1W2MTEnrcdzqSXRUTMbpxHc4I34mNCRP7tQ0iIvIzJVoSvTE8E96u0pigrqNbS9w9FCKv55aFRXsjISFBtXc50nTLmJgYtbioXW1tLQID2d+ZDqOlCXhrLlC4DgiOAa74CIju34GZs/2vcTz22FKQgEo8pnsMBluLSz9P2iBNiLbAZrXgxwNNWLu/wqWfR0RERH7qiz8Dheu1WX8XvQQM0nos68JOUNejGtchWq+11iMiIjrs4tfFWjuXRlWJ7kNJdPluROT9SfQFCxbgzTffbL+/cuVK6PX6I7ZamTp1qnqt3fr165GaOkjVzOR9LC3Ae1cD+74HAsOBX34AxI+CJ6huMWB1Zai6/bvADzDEmo9ptV17pztbVKANdZu/Urf//oXD6qVEREREzrDlfeDn57Tbv3gOiBq8VorFgRk4EDgMBlhxSpBWWUhERNSjumKgsUItft0U7hnFdgNRZYxVM91NtiaEWOvcPRwir+YxSfQJEybg7rvvxtdff42lS5fihhtuwBVXXIGQkBD1vLR1aWnpWpV77rnnYsWKFfjqq6/U8w8//DBOP/10N3wD8nhWK7D4ZmDHp4DRBMx7G0iZBE/xXVk4LDYd0oObYYrV+rlPrV2GyEHoXVa94m0Y9cDKPeVYsbvM5Z9HREREfqJiD/C/tvVdZv4WyDp10Idgr0Y/0bQdgYZB/3giIvImbVXoiBkOm8H7WwFbdIGoMWhrv7GlC5GPJNHnz5+PSy+9FBdeeCHmzp2LM844A//85z/bnx8/fjyWLFnS5X2yCOnjjz+Os846C4mJidixY4dKxhN1INOWPvs9sOm/gN4IXPIqkHk8PMX+hkDsbQiCHjacGFeL3OCjsDcoGwZYMKv6Y5d/vqW2FKcN005YPfLFDtg4zYuIiIgGymoBPrwRMNcBQ44DTvyTW4axxzQWdfpIhOubce4oj1kSioiIPFFJjnadkA1fwb7oRD6QRJdEXWbmoYUaFi1ahKqqKpSXl+OJJ55AaKjW2kLs27cP559/frfbkap1SZ6/8cYb2LRpk0qmE3Ww7IG2acQ64IL/ACM9Z7aC1QZ8Xx6mbk+IbEBMoEWalWN55HmwQo8RTZuR3rzL5eO4MDsMpgA9NuRX4ftdrEYnIiKiAfrxKSB/ldZC74J/Awb3JLBtOgO2hkxVt381kWsnERHRkRcVReJY+IpKY7y6jh6EWe5EvsxjKtEHaujQoTjzzDMRFqYlI4narXgS+P7v2u05jwJHXQRPsq02GOXmAJj0VkyLPrTgVUVAIjaGHqtun1D1EXQ2q0vHER1swNxpWhuZf32b69LPIiIiIt8WXJ0LfPM37c6ZDwJR2j6Gu2wNna6uTx9hQEgLF1InIqIjtHNJGANfUcFKdCKn8JkkOlG31r4MfHmPdvuU+4Cpv4YnabECKyu0GRfToutgMnRso7Iq/Aw06YIR31qIsQ0/uXw8184cBqNep3qjr8+rdPnnERERke+RvuND1i8ELGZg1FnAxMvdPSRUG+OQ05IMvU6HYTWr3D0cIiLy1DZkpdt9sBKdSXQiZ2ASnXzXhjeBj3+j3T7udmDmnfA0G6tD0GAxINLYivGRjV2ebzKEYlWE1nrm2JpPEWhtcul4UqKCcf6kVHX7GVajExERUT/cd0IQQmr2ACGxwDlPqDZ1nmB500h1Paz2J229HCIiIkcVe4HWJsAYDEQfaj3sK+1cIlvLobdZ3D0cIq/FJDr5pOiCZcDim6ULJjDtOmD2/fA0zRYd1lRpVegzYuph6OH4cmPo8Wr6Vai1DtNqv3T5uG44Ybg61v1yWzFyS+tc/nlE5NsWL16MYcOGwWg0YuLEicjJ0RZr2rJlC6ZOnYro6GgsWLCACxoT+YjglnL84bi2vuOSQA/Tqt88wRpzJurNNoS3lAKF6909HCIi8jT2KvS4LEBvgK+oM0SiRRcIA6wqkU5E/cMkOvmcX2Qbkbnub4D0EJ98JXDGQx5TAeVobVUImq16xAa0YmRYzxXmVp0ByyPOVbcn1S1HqKXKpeMakRCGU0ZrB7yv/LjPpZ9FRL4tNzcXV199NR588EEUFBRg5MiRuOaaa9Dc3IxzzjkHU6ZMwZo1a7Bt2za8/PLL7h4uEQ2UzYqM6lUw6HWoSD0ZyD4HnsSMAHy8s1W7s+V9dw+HiIg8TdlO7Tp+FHyKTu+wuChbuhD1F5Po5FMim/Lw9oXB2iKc0n/z7H8Aes/7v3mTRYcN1SHq9oyYOuiPkOPfaxqDA4HDYEQrptd+5fLxXX3cUHX93toDqG5scfnnEZFvkqpzSaBfcsklSExMxI033oj169fjs88+Q3V1NR577DEMHz4cCxcuxAsvvNDjdiTpXlNT0+FCRB6ocB1CWypQ1WTDgbEyI9DzvL2lbb9m60eA1bWLthMRkZcm0eN8LIneoS96qbuHQuS1PC+7SNRfxVsxrHI5Agxt1U/nPuWRCXQhCfQWmx5xgS0YHtp85DfodPgx4ix1c1z9SiQGNLh0fMcOj8WoxHA0mC145+d8l34WEfmus88+G9ddd137/R07diArKwsbN27EjBkzEBKinUwcP368qkbvyaJFixAZGdl+SU9PH5TxE1EfNNcCe5erm3d93YRWUww80ee7W2HWm4CaA8CB1e4eDhEReZLSHdp1vLaGhi9hJTrRwHlmhpGor6Sv5faPoYMNL28wY9+kP3lsDzMLDO1V6FOj63vdaaYgaDj2B41Ufcwui2s7Q+4iOp0OVx+nLaTyysp9sFjZq5iIBsZsNuPRRx/FDTfcoCrJhw7VZrzY/80xGAyorKzs9r133XWXqly3X/LzeXKPyOPs/gqwmFEfEIf/rPHcWWzNFuBA6ATtzub33D0cIiLyFLI+T9kuP6hEZxKdqL+YRCfvl/8TsOsLdbMkZBR+tbjJYxPootiUoXqhRwe0YkRvqtAd2KvRT47Mx8hY1/7ne/6kVESFBOBAZSOW7+KULyIamPvuuw+hoaGqJ7osMhoUFNTheZPJhIaG7mfZyGsjIiI6XIjIg5TnAmVSvadDXuR0Wdbdo+0Pn6Ld2P4JW7oQEZGmphAw1wI6AxAzDL6GlehEA8ckOnkv6XueuwzY8412P30GDkRM9egDN50xEAeDtYB8dFT9EXuhd3YwcAhyTWNh0AF/OaFjAsrZTAEG/GJSmrr91k95Lv0sIvJty5Ytw9NPP40333wTAQEBiImJQWlpx5NztbW1CAwMdNsYiaifrK3A7qXa7bSpaAzwzDYujg4GjwICw4DaIm02IxERkToZDC2Bbgz02Ur0UGsdgqyN7h4OkVdiEp28k8UMbP3wUC/LoScAw05UvcM9WehRp6JFH4RwowWjwpv6tY0fI85U13OPCkCmvhiuNHea1nf46+0lKK7p33iJyL/t3bsXc+fOVUn0MWPGqMemTp2KlStXdniNLB4qyXUi8jIFa4Gmai0pnXk8vIFVHwBknXqoGp2IiKi0rWVqvO+1chGyHkidXpvNGcVqdKJ+MfbvbURuXrhqy3tAXbE21WrUWUDi2A4vycnJGfDHOGMbjizQI3LGher2lKh6VU3eH2UBqVhek4JZEYWYG7Qcq3Bo0T5XfN/suADklLXgif/9hIvGhPdqW3FxccjIyHDKuIjIezU2NqrFRc877zxccMEFqKurU4/PnDlT9UV/6aWXcPXVV2PhwoWYPXu26otORF6kpQHY/6N2e+gswOBFlXujz9YKMnZ8Csy+z92jISIiT6lEj/O9RUUdq9HDzDWIaS1BceAQdw+HyOswiU7epfYgsOV9rVdZQAgw9hdApNZyRBRV1EJy0/Pnz3feR7YlfQZqt2k0jOEJCLA2YWz4wKZPvVk6EseHF+CYgB3YVb8b5aEj+r0te1Krp98sdOxJiDv7t3h1RS4W/fIa6aNzxG2GhAQjJ2c7E+lEfm7p0qXYtm2bujz33HMdKs+ff/55VaG+YMEC6PV6fPvtt24dKxH1w/4VgKUZCEsAEsfBq4yYDeiNQOl2oGw3ENf/fSkiIvIBPl6Jbu+Lnm7ejehWrnlG1B9MopP3kJWyc/4HWFuAkFhg3MVAcFSHl1TVNakU7z9vOg3HjM8a0Md9unon7nnxSzQ1DbyNidUGbA6erG4nN+6FUT+wlgUHzOF4f1srLh4bgGkHXsJno/7W723Zv9///epUnDWt61l3WQT1mvwW1Ecm4vnH7sWkkLLDbi8nrxTzF76DsrIyJtGJ/JxUoNts3Z94y8zMRG5uLtauXYsZM2YgNjZ20MdHRAPQUHGon/iwkwGdl3WJlH3IzJna2jo7lgBxt7t7RERE5E5+UokuolvYzoWoP5hEJ88nCZgDPwN7lmn3ozOBMecDRlOPbxmREo3JI1MH9LGSDHaW78vDUGWMhbW5AfHN+bJayYC3+bfvm1USfWTZV1iZcR2qggc2HWtoUs+/2cW2arycF4efbSPx65GuXdCUiPxHUlIS5syZ4+5hEFF/7P1WW+Q9Zri2b+aNRs9pS6J/BhzHJDoRkV+fGK4v9f0kekC8uo5mT3SifvGykhHyO1YLsOuLQwn05IlaBfphEuie6MX9ceq6btNSGG2tTtnmxmIrVrdkQQ8rph54Ba40N61cXX9VGoGSZp57IyIi8mtVeUCZTHvXAcNOgtfKOk27zl8NNFa6ezREROTOWe8iIhUICoOvqrBXoreWaSfCiahPmEQnz9XaBGx5FyjacGiqcNbpgN67Fp7bXReE78oiVEV97dqPnbrtd83Hqevs0k8R3lQEVxkV3qwWQ2216fBuQbTLPoeIiIi8YIbgnm8PFTeEaoUCXil6CBA3CrBZgNxv3D0aIiJyFz9o5SJqDDGwwAAjWhBuqXL3cIi8DpPo5Jkaq4D1rwGV+wB9ADD2QiB9GqCTZUPhlVXoQ8y5aK0uduq2d1jSkBc5FQabBUcXvApXmptWoa7/eyBGHT8TERGRH6rcC9QWaotyZh4Pr5d1qna9+yt3j4SIiNyldIfPLyoqbDqDajMruLgoUd8xiU6ep7oAWP8q0FAOBIYBEy8H4ga2SKi7VJoN+KBQq9we19hWUe9kP6X/Wtt+8f8Qaj78op8DMSepCqEGC/Iag/BzVYjLPoeIiIg8lJxF3/eDdjtlMhAYCq9nb+my60vAyqntRER+SbUo8/1KdFFt0Ir8Ii1ay1Yi6j0m0cnzgtfGN4GWBiAsEZh8JRCeBG/11oEYNFn1GBveiKSWApd8xoGIySgMHw+jzYzJBa/DVYINNpyVVK1uf8CWLkRERP5HZgjaq9BlhqAvyDhGK9qoLwEObnT3aIiIyB38pBJdVBm1JHqU9EUnoj7hCoHkMeLqdwJFq6XMCYgdAWSfCxgC4a1arMCredpUqV8NKUXOHhd9kE6Hn9J+hQtyfoPxBz/E6vRfo9kY7pKPujClEu8WxGBJcRT+kl0Ik4F9XYiIiPymCn3/D4d6oUvi2Uvt3bsX69ata78/LGYiog7+gMLvXsHBUb3ft4mLi0NGRoaLRklERIOipVFbMFvIOhk+jkl0ov5jEp3cz2bDvScEIqPmJ+1+0gRg5OmAzrsnSnxWHImDzYGIC2zB2cnVyHHhZ+2LPhZlIcMQ17AHRx38EGvSrnDJ50yLrkeqyYyCpkAsLYnAuclaZToRERH5uKr9QE1BWxX6DHijutoadX3PPfeoi921kwPw7DnB2L30OZww74leby84JATbc3KYSCci8mZlu7RCvuBo714su5eYRCfqPybRyb2sVqRvfgL3n2jS7mccC2TO9MoFRDt7LU8LTvPTyxGkd3HFtk6HtSnzcfruv2JS0dtYlzIXVlmQ1cn0Oq0a/ck9iXi/MJpJdCIiIr+rQp8ABHlnFXpzY6O6njXvNhx93Intj8e2lgAVv8exQ4Lwx6eeh1nftl96GMV5uXjjoQUoKytjEp2IyCf6oY/yiTxEb5PokZYyLb77wXcmchYm0cl95B/sz36P+H2LYbXZcCByOjKGzoIv2Fkni2+GwqCzYV56xaB85vb4M3Bc3r8QZi7F6NLPsS3xHJd8zgVtSfTvy8JR0mxEQlCrSz6HiIiIPIRMc68+AOgMXluF7igyMQ1pWWMPPWAbg+q1KYhsLsS0hCbsi57izuEREZFb+qH7/qKiotYQAyv0CLC1INRag3pDpLuHROQ1vLtfBnl3An3p3cDPz8EGHa76qAllob7Tf+zNfK0X+qnxNYOWZJbK8/XJl6rbUwpf135jFxgaasaUqHpYocNHhVEu+QwiIiLyIHk/OlShu2bdFbfS6ZAXOVXdTK/62d2jISKiwVS2w2/6oQurzoAaQ7S6zZYuRH3DJDoNPknufv1XYOU/1d28Cb/Fa5ta4CsaLTrV6kRcnl4+qJ+9OekXMOtDVG/0zMq2A14X+EVKpbqW7+miXD0RERF5groSrR86dED6dPiq/CgtiZ5RzSQ6EZH/9USXSnT/SKIL9kUn6h8m0WnwLf878MNj2u2z/o7yIXPgSz4+GIXaVgMygptxXGzdoH52szEcm5POP1SN7iJnJ1UjUG/FjrpgbK09ct9QIiIi8lIFPx9KLpjcP+W7wmzAmsoQfFwUiTfyY9TlvYJo/FAehvyGgH6f3M+PPFpdJ9TvhKmlyrmDJiIiz2RpBcp3a7fjsuAvmEQn6h8m0WlwbXgL+OYB7fbpC4Fp18LXvNHWykV6octCnINNFhW16AzIqF6DhLocl3xGZIBFtaoR7xdoVfdERETkY8z1QPE27XaqVqntLoVNAaqN3Gv5cVhREY49DSaUmQPUpaApEGurQvFBUQzeOhCD3XVBfd5+Q2AsSkNGqNusRici8hPVeYDFDBhNQKT/LBJdbXBYXJSIeo0Li9Lg2fcD8L9btdvH/QY45mb4mi01JmysDkGAzoqLUwdnQdHO6oKSsDPuNGSXfoajC17Dp6MWuuRzLkytxJLiKFV5/+dRRTDylBwREZFvKVwH2CxAeAoQmeqWIegCg7EzcCS+K4hpe8SGzBAz0oPNiAlslSYzqGvVo7ApELvqglBqDlD7J6PqG3FSXC2CDL0vTc+PnIr4ht2qL/rOuFNd9p2IiMi18vLyUFZ25ARxRPFPkNOnjcEpyNmwocvzOTmuKUpzN1aiE/UPk+g0OKrygXeuAKwtwJjzgFPugy+yLyh6RmINYgMtbhvHmtT5KomeVfY1IoYUoMbk/APfmbG1iA1sVRVgMoX6xPjBbV1DRERELmRtBQrXa7fT3FOFXtqsR/IVj6MoIE3dzw5vxPToejUjrrOxEU2YGatTFelrq0JUy7mDzQH4RXIlIgKsvfq8vKipmFz0FivRiYi8PIGenT0aDQ2NR3ztbdMD8cQZJiz5aQcu/t2UHl9XW1fnu0l0LnJG1GtMopPrtTRpCfSGciBpPHDBfwC975UtSxXU4qIotywo2llZ6Ejsi5qBzKpVmFz4Fr4d9junf0aAHjgnqQov58Xho6JoJtGJiIh8Sck2oKUBCIpwy2JrK3aX4d+7wxEQG4EgaxPmpDUgPfjwC9GbDDa1Hs2w0GZ8VhyJ6hYj3i2IUQuiR/eiuKEgYhKsMCCq6QAimgpRY0px4jciIqLBIBXokkB//U+XIDsj/rCvTateDTTswPFTJ2LtKZO7PP/p6p2458Uv0dTUBF9SbYyFDToE2ZoQbK1393CIvAaT6H6kt1OaeqO5uRlBQb3rN5m+6XHEF65Da0AEto+7C+bNOT45RUoS6PUWA4aHNqkqKXdbmzpfJdHHFS/GqvRr0BSgJfid6fzkSpVE/6IkEvWtBQg19q7Si4iIiDyYVKUdaKvGTpkM6Aa3+OG7naW49tU1MFt1aDqwFcdEVyA9eGSv359sasElqRX4oDAalS1GdX1p2pHb7JmNYTgYPhYptZuQXv0ztprOG+A3ISIid5EE+uSRR5iRvakZaACS0oYgKanra3PySuGLLLoA1BkiEW6pYl90oj5gEt1P9GVKU2/odL2b9XPeKCM+uixE3Z7zUhGW3j3HZ6dIvdu2wObctAr1+7hbXuQ0lISOREL9Tow/+AFWp//K6Z8xIbIRQ0OasbchCF+UROAXKVVO/wwiIiIaZFX7gfpSQB8AJE8c1I/+YVeZlkBvtWJ0eAu+ePvPCLzxtj5vJ8xoxUUpFXi/MAYVLUa1hkucznDE9+VFTlVJ9Iyqn7E1kUl0IiKf1tB2gjXYvu6G/6gyxKkkutYXPdjdwyHyCkyi+4m+TGk6EvuUpn/edBqOGZ/V4+sCLA3ILv0YsJlRHDoGixZMwaLDbM+bp0jtrgvChupQGHU2VZ3tEXQ6rE25HGfuug8TDr6HNalXwKp37n/ycrJAvu/juUn4sDCaSXQiIiJfULBWu046CggwDdrHbiuswQ2vr1UJ9NPGJOI43Q58YWnt9/ZCjDack1yF/x6IQUlzAJrjpgF49Yh90WcceEFVoquKEU+ojCAiItes/dFco90O1grifJmEtBabDgE6mwpt0hc93by7LYme7u7hEXkFJtH9TK+mNB2BfUrTiJTonrcl/0Jvfkcl0BGWiMRJc5Co7776xxemSNmr0E+Mq0FckPsWFO1sZ9ypmLXvSYSZS5FV/jV2xJ/u9M84P6VKJdFXlIehpJn/pBAREXm15jqgfLd2O2VSvzfT13Z9ZQ0W/PHrMtQ1WzEuPhC/ztbhyy/2YqCiAiw4O6lKtXSpDstE6JgTD/v6g+FHoUUfhNCWCsQ25KI8dMSAx0BERB6osa34zRAEBGiz531RTYsem2pkwW0T6loN0MOG6MBWNARNRbZtNZPoRH3AjBe5RvFmoHIvINNms88Fekig+4JWK9SBmbg41UOq0NtY9QHYmHwRjs37DyYVvuWSJPqQEDMmR9VjXVUo/lcUhcnY7/TPICIiokHch4MNiEgFQvs+e7GoolZdz58/v/dvMhiRNO9BBKWMhrlsPz77x++xpPnQ+jINzf2vRhepwS2YHlOPlRVhiDntRphR0ONrLfpAtcCorCuTUb2aSXQiIl/V2NbKJSTGZ2cd7akPVOuXma2H1jaxQodycwCeMx+DZboMPGh8061jJPImTKKTayqYcr/WbmfOBEJi4cu+Lw9HqTkAMQGtOCleO3D0JJsSf4Fp+S8iuW4rkmo3qworZ7sguVIl0T+SJLpv/7mJiIh8l8wkLNqo3U6e0K9NVNVp7fnmXP9njBo/pVfvWV9hwJ46AwL0NpwxPhmhj72mHl/99Sf44YMX0dwysCS6ODqqHhsKm9BoisMBawpsNpnO3n3SJD/yaJVET61ej/Up8wb82URE5IEaKn26lcv6qhAsLw9XtxODWjAlqh7pwWa1aPeuOhPWV5mQa03FDfXXY4h+u7uHS+QVmEQn58v9CmhtBsKTgHTpPenb7K1czk+pRKC+F6utDrLGwBhsjz8D40o+xuTCt/DpKOcn0c9Oqsb921OxpSYE+eFhTt8+ERERuV6YuRhoqgIMgUD86AFtKzZlCNKyxh7xdTsO1mJP3kF1+8yjUjE0LvTQc5vaerM7gV4HpJWuxM7k01FnDMOukjqMTNSSC50diJysrtNq1rMvOhGRr1ei++Ciorn1Qe0J9PERDZgVVwtDWygzGWyYEt2Ao0Kr8N0BC7bZMlFtmgRjdIp7B03kBQ7N6XDDQpdDhw7Fvn372h9bvHgxhg0bBqPRiIkTJ/a6l+K5556rKknsl9mzZ7tw5HRYlfuAUjmLqQNGngno3PZ/sUFRaTbgq5IIdfuiFM9q5eJofcpl6jqrbBnCmoudvv3oQIvqBy++q2PwJSIi8kZxDbu0GwljtES6i9U0tmDZ9hJ1e1pmTIcEuisEtdahetV76vb3u8rUAqbdKQkdjRa9CcGt1YhpHHhfdiJv09Nx+ZYtWzB16lRER0djwYIFakYHkU+0c/Ehpc1GfFGs5SgmRDSo2fL2BLqjwMBAvGR6HJN0u9CqC0D8+XfB3H1YJKI2encl0M8+++wOCfTc3FxcffXVePDBB1FQUICRI0fimmuu6dX21qxZg82bN6OyslJdJOiTG1gtwK6l2u3UyWpBUV8nPcDNNj3GhjdiTIQ2fdkTlYWORH7EFOhhwYSid13yGRekVKnr5SqJzootIiIibxITrENUU552J3miyz/ParPhi20HYbZYkRxpwvShg5PEqPnpPQTazKhrbsXP+yq6H5s+AEVt7e/SqtcPyriIPEVPx+XNzc0455xzMGXKFHX8vW3bNrz88svuHi6RE9q5+E4S3WIDviiJQItNj/TgZlWBftjXB4TiX4H/QIitAYEJQ/FxYfCgjZXIG7kliX7ZZZdh3ryO/QXl7LYE6ksuuQSJiYm48cYbsX79kXdaJbDLGfBx48YhKipKXUJDXVvFQj0oWKOdzQ0I1Xqh+4H32hYUvSi1+4MwT6xGP6r4Ixgtzk/4nxJfg3CjBWWWYASljXH69omIiMh15o8PgB5WICxhUAohNuRVobCqCQEGHU4fmwS99FsZBLZWM5JbtfYx6/OrUNfUfb/1AxGT1HWqtHQh8iM9HZd/9tlnqK6uxmOPPYbhw4dj4cKFeOGFF9w9XKL+kfazLfU+1xN9Q3WIWjTUpLfizMRq1crscKoMcUjSVeKXtiWwWS1YVxmE73eVDtZwibyOW5Lozz33HG677bYOj0ll+nXXXdd+f8eOHcjKyjritlavXg2LxYK0tDSVPJcEvVSj90TOoNfU1HS4kBO0NAL7f9RuDzsBMJrg67bXmrC5JgQBOivOS9aqsD3ZnpiZqA5KUVOTs0s/dfr2pbeaBGoROvYkp2+fiIiIXMRmw7WTA7TbSRNd3gO8urEFK/eUq9uzsuIRGdz22YMkwlaHlEgTLFYbftqrjaOzgra+6CqJzpYV5Ed6Oi7fuHEjZsyYgZCQEPX4+PHjVTV6T3jcTR6tsS1nJAWAxiD4gpoWPVZVaOuTzYytRbDhyLGryhivro/Tb0Xtuk/U7fsWb0Vzq8XFoyXyTm5Joksv9MMxm8149NFHccMNNxxxW9u3b8eECROwZMkSrFq1Cnv37sVdd93V4+sXLVqEyMjI9kt6enq/vgN1sn8FYGkGQhOAxHHwBx8WRqnrk+NrERPo+UHGpjNgffKl6vakwrddckB4frK2MxI6+niYZS4ZERERebyQyhyMSzDACgOQ6NrZZDKDVPqgt1ptSIsOxtgUrW/rYJJTBMeOiFO3txbVoLLB3OU1RWFjYdEZEW4uQWRzwaCPkcgTOB6XSxLc8The1iIzGAw9FrDxuJu8ox+671Shr6gIQ6tNh1STGdnhvZt5XmXUYmGioQZV37+BMKMVe8rq8cIPXA+EqDseuerjfffdp6rKe9MTXRLmX375pUqkH3XUUXjkkUfw3nvvHfb1Mg3NfsnPz3fy6P30LG7hOu32sJN8fjFRYbUBHx/UkugXePCCop1tTTwXZn0IYhv3IqN6tdO3PyOmHrGGRuhNYVhX5Lk94omIiOiQ2PzP1XVl8BCXzybcfrAWeRUNMOh1OHl0gkrEuUNqVDAyY0NUTcFPe7u25bMYTDgYNlZ7Lfuik59yPC6XRUaDgjpW7JpMJjQ0NHT7Xh53k0drqPCpfugVZgN21mnx+4S42l5PKKsyxqrrBEMNbOYGnJHUqO7/c9luVNR3PcFM5O88Ltu5bNkyPP3003jzzTcREND3qZ0JCQkoLy9X08e6I4E/IiKiw4UGaN8PgM0KRA8FYg4/y8BX/FwZisKmQNUD/MQjLNbhSczGMGxNPOdQNbqTSc+1WWGF6vZ3+7UATERERB6s1Yzowu/UzYrgYS79qAZzK5bv1HqtykKi0SGBcKdjhmnJg50Ha1WLmc4KItkXnfxX5+PymJgYlJZ27JVcW1uLwMDu/zvmcTd5RTsXH0mi/1Qp6wLqMDy0CfFB3a/1cbhK9Gh9A0ICgIlRLWqGWIPZgue/3+PCERN5J49Koksrlrlz56pgPWZM76aSXnrppfjhhx/a769cuVItgNL5LDm5SH0ZULJVuz30BPiLj4q0KvQzEqtVL3BvIi1dbNBhWOUPiGrc7/Tt25Poa4uaUdXN9GgiIiLyILnLYGypQVGtFbWBrl1QdPnOMjS1WhEXFojJGe6fQp8QYcKQmBDIntza/ZU9Li6axiQ6+ZnujsunTp2qjrUdXyOFa5JcJ/I6PtTOpVEf2l6FPj26bbHUXmrWh6JJp61zMCxaryrYbz9FW5vwlR/3sRqdyFOT6I2NjWoRk/POOw8XXHAB6urq1EX6JgrpwdbS0rVCRFq43HHHHSqR/tFHH6lpY7KCOA1iL3QRmwWEJ8EfmK06fHowUt0+3wsWFO2sOjgde6OPU7cnFPXc+qi/hgTWwVy8B61WYMnmIqdvn4iIiJxo8zvq6u2tLS5tybe/vB47imtVP/LZ2YmqnYsnODpTS6BsK6pBfXPH6r2i8PGwQo+opgMIbS5x0wiJPOO4fObMmeqY/KWXXlKvW7hwIWbPnq36ohN5Fckx+VA7lyI1i6zvVeidq9FHxGj7AKeOScSY5AjUsxqdyHOT6EuXLlWrez/33HMIDw9vv+zfv7999W9ZPLSzP/zhD+q5M844QyXPb7rpJvz5z392wzfwP2G2WqA0R7uTeTz8xfKyMFS3GpEQ1IIZMXXwRhuSL1HXY0o+QZCt+9ZHA1G/7Rt1/dF6LsRFRETksZrrgO2fqptvbu5arOIsFqtNVaGLCWlRSIxwbd/1vvZGT440qTGuz6/q0gavNHSk9rqaDW4aIZFnHJcXFBTg+eefxy233IK4uDgsXrwYDz30kLuHS9R3LQ2Ape0Y2KTNMPdWelM4yoJS1O3JUd2vT9Dbvuj2JLqsVfKb2Vo1+msr96Ou0wlmIn9mdOeH26vMhZzpdrzf2b59+7p9XPqzvfDCC+pCg2uoJVe7ETcKCHPt9F9P8lGRVrF0TlIVDJ5RRNVn+6Omo9KUgeimPByjW4fnnLz9+m3LEXPSr/HzvkrkVzQgPUabIkZERETOkZeXh7IyLTHdX9EHvsTQ1kbUBMRjTWENnKmyqgpFRdqMtJ3lZlQ0mBFkADJDW9ofP5KaWtevOyPJgqOHROPjTUXYUlCterUHGA7VGRVETEJi/Xak1azDzvjTXD4eInc73HF5ZmYmcnNzsXbtWsyYMQOxsVryjcgr+6EHRQCGvq/D50nCxp8Km86A+MAWJAf172R450p0tM0YGxYXij1l9Xh/7QFceWym08ZM5M3cmkQn7zU0SodkW9sBUMYx8Bd1rXp8VaItinOeF7ZyaafTY2PyhThx7+OYbfvR6Zu31JXjqIRAbCoxY/GGAtxysnYmm4iIiJyTQM/OHo2GhoEt4v3J3GAMHRmAx786oO7X1g18hl1ZtdaP9Ztly7B89QbojEGIOPoc6IyBqNz+E17+rq0IoxfMJXvVdWura6vgMuNCERkcoBYX3V5Ui6PStLZ94kDkJEwueouLixK1SUpKwpw5c9w9DCIn9EP37lYuVugQPuksdXtCZIPqZ94f1QYtiT48Wo/itsf0eh2uOi4T9y7eipd/3IdfzhiiHiPyd0yiU78sOC5I9bRE9DC/6YUulpZEoMmqx9CQZhwVMbADV3fblnAOjtv/L6RbizAzw/m9DE/IDFZJ9A/XF+Dmk0aoSi8iIiIaOKlAlwT663+6BNkZ8f3ahtHShKNKZG0UG+JHTgW+/QFNTU0DHltto7YI2XFj0zD+qLFY05CAPeZARBmacNHRMdDrep+0WLasHmt3STsYC1xJr9NhQloklu8qw4YDVRiXGtG+31IYMVFdxzXsgamlCk0B3j31n4jI7/lIP/QDgZkwRibBYDVjZFj/47djJbo9iS4unJyGRz7fgb1l9fhuVylOGpXghFETeTcm0anPTGjGhRPbpj1lzICv21VYgfidWm/v1w4mq+tpgXlYv6tv/b4Ly5w7TXqgmo3hyIk/E+OLP8Qt0wKx82Al1rV9z4HIyStV19NTTXguoBa5pfXYUlDToaqLiIiIBk4S6JNHpvbvzQXrgBIbEJaEyDitn6ozRYYEwhAWgz1V2gy+2YkNSA3WbvdWiCkQg2VMSgRW7ilHRb0Z+ZWNyGhrRdcYEI3y4KGIbdyrqtFzY08atDEREZEL27kEa21avdUO0xh1Hd98AAH60AEn0TMidVhpPdQSJjTIiEumpuOFH/bi5RX7mEQnYhKd+mOkPg8mgw6VuihER6bDV9mnI9/6zJcAvoQ+JBJpN58mnVDw2MOP4OHKwn5tt9HsOQtzbEy+WCXRLxhtROYTX+GeF+W7Okd1WTFOHZOCjzcW4oP1B5hEJyIi8iQl27TrhDFAWz7BmaSj8ndl4dJDTlXIpQa7buFSZwgyGjAmOQIbD1RjY35VexLd3hddkugpNRuZRCci8pV2Ll5ciV5hNiA/cGh7Eh0Y1e9tNejD0WQzwqRrRVhreYfnrjhmiEqiL99VioKqRrUYN5E/YxKd+sbSgix9nrq5Xz8U0T7cosM+HXn2+Rdj/Nhs5FoTsMFqQBTqceFN1/R5e98s/xHrv/8Kza2unZLcF2WhWdhiTsG4wEL8be5EbI4/Z8Db3LFrD5a88zqqqqpwwZSjVRJdLn8+KxtGh4W6iIiIyE2aqoAarQ86ErKBHb3vU95blYFJKGwKhFFnw/Gxrl8g1BmOSo1USfS95fWoa25FWJB2qFQYMQHjiz9ASu0mdw+RiIgGQhbNtVeie3FP9I+LomDVGdB8cDdCAga4nolOh2JLBIYYKxDWos0qtxsSG4pjhsWqmVrvrTmA22dzrTPyb0yiU9+UbIVJ14K9lVYUxyfCH8TExSNtyBD8VBANNAFjY6xIix7S5+2EReXAE33SMF4l0c+NL0B1RiqsuoH9s1BWfSiIz8yKR0xoIMrqzPh+dxmngBEREXmC0h3adWQGECTV4k6mNyIvRKuKmxxVj3CjFd4gNiwIKZEmFFY3YVthDaYN1RIshRHj1XViXQ4MliZYDCY3j5SIiPqluQawtkJNLzd570zp9wu1VjT1W5YBk9IGvL2StiR6uLljEl1cOjVdJdHfXZuPW08ewQVGya+xLJT6dtb2wBp186nVZnXG0l80tOpQ0KT1gc8awKIdnmhl0zAU1loRY2zCiEbnVlgFGPQ4d4LWZ/X9tW0Vb0REROQZSfT40S7ZfPjEM9BsCEWwwYIpUQ3wJlKNLrYUVsMq+77Soi4oFfUBsTDYWpFU55lFEURE1Av2KnRTlJZI90K76oKwqSYEOpsF9TnfOWWbJVZtzZLwTpXo4oxxSQg3GXGgslEl04n8mXf+q0HuUbUfaChDi82AF9ZrrU78xe56E2zQISGoBZEB3lFN1VsWGPCftdrfc2L9D07f/kVTtDPjS7cWo6rBv/5/Q0RE5HGaa4HatnVd4pw/LdusC0TkcXPV7RnR9QjUa4lobzEiIQxBRj1qm1qRV952AkCnUy1dRErtRvcOkIiInNAP3XsXFV1cFKWu08z7YW2odso2pZ1LT0l0U4AB503UCuP++3O+Uz6PyFsxiU69V7heXe2xpqKmGX5lV32Qus4K9a0qdLtn17ag1aZDqnkv4s0FTt32uNRItVCX2WLF4g39W4yViIiInKR8l3YdnuKSVi4bg6fAEBIJk6UO4yIa4W1k/ZbsZC2ZsK2opv3xgvC2JHoNk+hERF6rocKr+6HLBKklB7Uk+vDmnU7brj2J3rknut3FU9LV9dJtB1Hf3Oq0zyXyNkyiU++Y69oPunZbtX9A/UWLLhAFjYE+2crF7mCdDT82ZKjbE1xQjX7J0Vo1+jtreOaaiIjIrUrbDrrjRjp900VNAdgSMlndTq/fDm9tm5qdpJ1c2FNWj+YWbUF4eyV6cu1mwOZbsxKJiPyunUuwdybRc2pN2NsQhEC9FRnmPU7brr2dS1hLOWDpmiQfnxaJoXGhaGqxqkQ6kb9iEp1652DbAUN4CqrgggWoPFhFYKLPtnJx9GmttgDY6Ma1CLI6t3/peRNTEWjQY2thDbYUOGfKGREREfVRSxNQneeyJPqjuxJh0RnRlL8F0S0l8Fbx4UFqYXSL1YZdJdqC6aWho9CiD0JwazViGve7e4hERDSgdi7emUT/tFhbt+OEuFoE2lqctt1KayiaWm3QwwpUdy180+l07WudcXY5+TMm0al3c4aK2qaupkyEv6kITPbpVi5225rjUWpMRoCtBdkN2gKyzhIdGojTxiaq2++yGp2IiMg9KnZrRRGh8U6fyr6jNgjvF2o9Ziu/eRFeWoTeniwY3VaNvv1grbq26o04GDZW3WZLFyIiL2S1AI1V2u2QaK9My3za1srl7ETnFqZJ0eCeyraCwYruK9ztfdG/31WG8jo/6+9L1IZJdDqyyn1AUxVgCALis+FP9MERqAmIVbezwnw9UOiwKfQ4dWt8/Y9alHaiS47W2gB9tKEQTW1To4mIiGgQle7QrmOdv6Do33cnqYPwzOZdMBc5r0+ru9iT6AVVjahp1Kr9uLgoEZEXa5LEsw3QBwCB3je7fnudCXvaWrmcknBozQ5n2V1x+CT6sPgw1dZFZmkt2Vzk9M8n8gZGdw+AvEDRBu06cSxgCIA/CRl5jJQjISFQWrn4fuJ3e8gUzKz5H2Jbi5FqzkVB0Ainbfu4EXFIiTShsLoJX24rxjlt08GIiIiof3Lyul8ArDs6WysmlO9RFTQ5DdFo3HloIfG9B9t6xPbTuqoQfFkSCT1sOLp+Jb6DZ6utqUFR0ZETAAkhBpQ0WPDzrkKMiQ/E1tZ0TJfHK9ehzHLioIyViIic3colWh3je5svirW+5bNiaxFmtLowib63x9dIS5dNB6rx8cZCXHFMptPHQOTpmESnXi8oimT/a+USMvp4dT3CRxcU7cysN2F78BSMb1ipqtGdmUQ36HW4aEoanly2Wy0wyiQ6ERFR/9gTwPMXvtPr95w/2ogPLw3B3korxtz/VrevKamq7/NYZOLaI7uS1O2LUisRUTqwhLwr2WfCrVmzBuu3H3lBtsCEYQgZOQMb95fihw+XINJoxq9OBOJbCrH807fVa3qTjCciIg9Lonuhr0q1JPppic6vQu+QRK/sOYl+5lHJeGBJDtbsr0RpbbNaQ4TInzCJTr1bUDQiFQhLgD9p0plgyhjvJ61cDtkUeqxKomc1bsK3llo0Gpw31e2iKekqif7D7jI1PTo1Kthp2yYiIvIXVVVaT9c5l8zHqKxhvXrPXP3nMucMeZFTcOefT+jw3JpNOVj+8buobuj7/s4P5WFYWRGGQJ0Vtw8vxkttExg9kblVS6JPGJ6AY6dOOuLrW2x6/K/aCoRE4qIzjkeMsRkHmzcgyVaCa44Oxd0/H/pbEBGRh2toO8nr5DVBBkNhYwC21IRABxtOiXdxEr2Hdi5Cjt+lpYtUo3+VU4y50zJcMhYiT8UkOh1hQdG2I6FkrQekP8kLHAad3oCQ1hpE+UErF7vSwDQUBQxBcst+jG1YjTXhpzht2xmxIThmWCxW7inHf1fn4c7TRjlt20RERP4mNiEBaUOGHPF1epsF44r2q1awxQnHIS2o43t2FZT1e1fx4bYq9PkZ5UgN1nqHe7pQUwCSY7WKviMZbjFjZ50JpYY4jI2tRUnlcCQ1lGBSeFtFIxEReVkluvcl0b9uq0KfEtWA2EDX5CY6tHOxWgF990sonj42SSXRP99ykEl08jtcWJR6VrVfW3zDDxcUFfuChqvraPNB+BupRj+0wKhz+63Nn6EduL/1cz7Mrc7v5UZEREQdpTXvhsnWiHp9GAoDhzptu58VR2JzTQhCDRbcPKwEvmh0WKO63llrgsUGFAZpv1+WsdjNIyMioj5prPTadi5LS7Qk+mwXLChql1dtg1VShJZmoLawx9edMU47ef5jbhmq2xbeJvIXTKJTz0q2atcJo/1uQdH6Vj0KArWzqjFm/ztI2hk8EU26YERaKjCkeYdTt33a2ETVO016qC3d5n8nKIiIiAZbZlOOut5rGgObzjm7/3Ie/O+7E9XtX2eWuawyzt2GhJgRrLei0apHXkNg+0mITGMZggzuHh0REfWKpQVorvHKdi61rXqsqghVt091YRJdThTXBcQesaXL8PgwZCWEocViwzfbffMEOlFP+rUX/fDDD6OlpeMZp2XLluGEEzr2VyQvDzKlbcnThLHwN8vLwmDRGdFSWYRgSy38Tas+ENtCph6qRneiAIO+fdrXqyv3O3XbRORcjPdEvmFos5ZE32dy3szCDwqjsafehOiAVlybWQpfpdcBI9sWmN9Vb0K1IRb1+nAE6Kw4OoVZdPI8jN1Eh6lCN5oAo3ety/V9Wbhao2NYSDOGh7p2rba6gPgjJtHtLV3EF1tZFEf+pV9J9LvuuguNjdrURrsxY8Zg1apVzhoXuVv5bsBiBoIigMh0+JulJZHqunHXKujgn+wtXYY1bUWYxbmLZs2blgGDXofVeyuw46D/naQg8haM90TeL7K1DDGtJWqK9v4g56xF0mTR4R+5WhX6TcNKEG707fZsWW1J9D31QbBA116NflwGk+jkeRi7iY7QD13nXUf435WFq+uTXLSgqKPa9iT63sO+zt7S5dsdpWg0++ZMNKIBLyy6fPlydW2z2bBixQqEhoa231+6dClGjx7dl82RN7RySRzrdUFG7CqsQPzOgn69t9Wmw9JirVKrYedKYPiJ8FS1NTUoKioa0DZqartPYlcGJCI/cATSzbtxVP1KrIw4s0/b3bt3L9atW9fj81NTgrDqQBMe/3gNrpuinbToTlxcHDIynLNgSV5eHsrK+rd4mqvHRuRJGO+JfK+VS0HgUJj1zqm+eyM/FoVNgUg2mfHL9HL4umRTC0IMFjRYDMhva+mS1bQJx6UbwFIA8hSM3US+1w9dFvD+rixM3Z4VVzuISfTDV6KPTYlAalQwCqoasXxXaXtlOpGv61MS/corr1TXOp0ON9xwA/Rtq/XK9YgRI/Dqq6+6ZpQ0uFoaDv2j6WWtXMqq69X1rc98CUAufWcaMgGJl50BS30Vmgu3o9F8PDxNU4t2tnfNmjVYv/3wAe5IzCXaWebW1tZuq9EliT6ufhV+Cj8NVt2RK67qarUz5Pfcc4+69MSUMR6Jcxfi021lePaWObCZO1bM2AWHhGB7Ts6Ak9WSQB+dnY3GhoYBbccVYyPyNIz3RL5jaJNzW7nUterx9J4Edfv24SUwGWzwddLSZURoMzbVhKiWLoVRWiX6sekGfCEZDiIPwNhNdBgNFV7ZD31XfRAONgciSG/F9Ggt1+FKtYG9q0SXf2ekGv2FH/biiy0HmUQnv2Hsa2WpPRBv3rwZERHaCsHkY0pyAJsVCEsEQuPgTWobzep69vkXY/zY/h0srrcMwR4bEFKXr36H5lbPm55kbhvThOEJOHbqpAFta9myeqzdBVisXb/n7uCjUF8dhjBrDYY2bUVu8Pgjbq+5bfrorHm34ejjeq7il2POpUU21CEE5/71LQwP7zoVvDgvF288tEBVjw80US3bkAT65X94BIkZwwe0LWePjcjTMN4T+Qaj1Yz05t3ti4o6wwv74lDRYlS9WS9KaUtK+IERYU0qiS4tXYriUmG2GRAXAkS0+N8C9OSZGLuJelOJHuOVrVxmxNQNyknr2gDtJLkqqpQD9sN0JbAn0b/KKUaLxarWPiPydX1KotudfvrpCAgIcP5oyDMU21u5jIO3iomLR9qQIX1+n8SJz/fHARYgotnzD4pCTQFIjh3YDnKIKbDH56w6I7aGzMC0uq/UAqO9SaLbRSamIS3r8DMZpgRX4budpcg3h2DWiAx1RtvVJIF+pHERkXPjvZxsmjp1Kr755htkZmaqx2677TY89dRT7a8ZPnw4du/Wkn1E5Bxp5t0wogU1hiiUGwdeJVZhNuC5fVqV2p1ZB2H0o+PlVFMLgvVWNFr1yGsKwZ7WeIwOOIj4xlx3D42oAx6rEx2uJ3q0VybRZ8XWDcrn1RvlJIMOaKkH6kqAcG39k+5MzohGXFggyurMWLWnHDOz2qrYiXxYv3Z9P/vsMwQHe9eKxtSHM7S1hdo/nAnOmfbrTYqbjai3GBCgs8JUP7Be475ic+gM2KBDZvMORLaWOnXb2UnhMOp1KK8340Bl9+1ciMh9nBHvJYF+9tlnY9++fR0el3ZUS5YsQWVlpbqsX79+gKMlop76oe8LynbKGjfP7ElAncWAseGNOCuxGv5EWroMb1tgdHd9EHa3aomF+KaBtdUjcjYeqxN10tKktaz1siR6Q6sOqyu0tQ1OGIR+6MKqDwAi03vVF92g1+HUMdoJ+i+2HhyM4RF5ZxL9o48+UpVkBoOh/SLTxuSafKQKPToTCNQWsPAnufUmdZ0ZYoZOWtoQaoyx2BekLUQ0vn6lU7cdFGDAmGStkn5tXtsUOyLyGM6I95dddhnmzZvX4TFZg2Hr1q2YNWsWoqKi1CU8XKu0ISInsdna+6E7o5VLcZMRr+XHqtsLsopUUtnfZIU2q+vcOhO2t2iJA1aik6fhsTpRD1Xokt8wBsFb/FQZBrNNj1STGcPb4s+giNHW/UDl4fuii1PHaO1fvtleqhYxJvJ1/Uqiy0Ilc+fOxfbt27Fnzx51kR5sck1eTP7RK7G3cvHPdhe59VpQHdQg5QVkgVExtmE1DLauC5AOxKSMKJn3gP3lDSir4+9O5EmcEe+fe+451brFkfRqtVqtmDhxoqqWO+OMM9Tivz1pbm5GTU1NhwsRHV50aymiLOVohQH5QVkD3t4zexPQbNXj6Kh6nBA3ONPKPU1qsBmmtpYuP1tHqcciWkqA+jJ3D42oHY/ViXrqh+49VegdWrnE1TpjMlnvxQzrVSW6OHZ4HEwBehRUNWJnsX/uG5B/6Xcnw2uvvRZZWVkYMmRIhwt5sbpiLcDIFJ64kfA3lWYDKluM0MOGzBAmcx3tNWWj1hCFYGs9RjRudOq2o0ICMTxBm/Wwbj+r0Yk8zUDj/dChbdUsDrZt24ZRo0bhtddew6ZNm2A0GnHdddf1uI1FixYhMjKy/ZKe3jbNlIh6lNm8TV0XBA1Hi35glXcFjQF4K19bjO3OEQcH92Degxh0hwot8g1p2Fbatih7/k/uHRhRJzxWJ+quH7p3LSq6vC2JPlitXPqTRDcFGFQiXXy93fPXlCNySxL9gQcewK233ory8vIBD4A8SOl27Tp2OGDoebFJX7W3Iai9yihoEFa+9iY2nQGbQ45RtyfUr3D69qcM0aoCdhTXorapxenbJ6L+cVW8v/zyy1VP9GOOOUYd5D/zzDP48ssve6wwv+uuu1BdXd1+yc/Pd+p4iHyRM1u5PL0nQU0pnxFdh2Nj6+HPRrT1RS8zxOP7/Lb9xbxV7h0UkQMeqxN10tCWRA/xniR6fkMA9jQEwaiz4diYwavwllkruVXa7foDW7Bu3bojXkaEaHHx4zV72h873AxTIm9m7M+b3njjDWzZsgUZGRnIzs5GRITW01gsW7bMmeOjwWzlYk+ix2nTU/3NnrZWLsNYhd6tLaHTMaP2C6Sa9yK2pQjlAclO23ZShAmpUcFqGtiG/Cqu7E3kIQYr3ickJKj2LkVFRR0+wy4oKEhdiKh3AqzNSGvWenXvlUVFB3gg/05BWxV6FhcOS5diC70VzQjCZ7UjcD32sRKdPAqP1Ym8v53Ld+VaFfrkqHpEBLh+rba6Wq2Q5Z577sHb8XpsuSkMLQe3Y8qUKUd8ryE8Hmk3vYRtJU2YetyJsDbVIjgkBNtzctS/Q0Tw9yT6VVdd5fyRkPtbuTRVAXqjVonuZ5osOhQ2BajbQ9kPvVv1hkjkmsYhq2kTjqpfiW+jfuH0anRJom8pqMG0oTEIMnLxIyJ3c1W8X7BgASZNmtS+4OjKlSvVomds00LkHGnm3TDAgmpDDKqMAzsx/dSeRLTadJgZW4tp0Q3wd9LSZWhIM7bXBWNT8FRAkuiF64GWJiBAW6CeyJ14rE7UqVjQC9u5fDfIrVyaGxvV9ax5t+GYY48BSq9HlEmHu596FQ16rfXq4XxZZEVNiwG/+OtrCCrfhTceWoCysjIm0cnn9CuJfuWVVzp/JORepTu06xj/bOWyryEINugQG9CKyEE40+vNC4xKEj274Wf8EDEHrQPsseooMzYEMaGBqKg3Y3NBNY4e4j07OUS+ylXxfsKECbj77ruRmJgIi8Wipp1fccUVCAkJccnnEfmbjKad6np/0CgMpIH5vvpAvF8Y3d4LnTTDQrUkenPadDTqP0OwpQ4o2gBkzHD30Ih4rE7kyFwHWMwAdEBwFLyB2arDj+Va4npW7OAu1hmZmIbEkZNRVx2PMHMpslPCUBw+9ojvG6krw5r9lagxRmNchv8VZZL/6FdPdKkWMxgM3V7IS8/OlrW1cokfDX9k74fOKvTDywvKQpUhFiZbE0Y1bnDqtnU6HaZkaAfq0tKl1cqTGUTu5qp4P3/+fFx66aW48MILMXfuXJxxxhn45z//6bRxE/m7jOZd6jrPNLCF4p/ITYTFpsPJ8TWYFKVVqREwJMQMnc2KgJgUrA2UanT2RSfPwWN1Igf2KnRTpDbr3gusrwpBvcWA2MBWjI1wT+ytMqWp66im3q1DlBkXqq73ldfDyuXlyIfp+7vYwJ49e9RF+q29/PLLGDt2LN58803nj5Bcr75E6xPmp61cLDapRNeq75lEPwKdHptDtQVGj6r/0embH5UUjrAgI+qbLdha0P0Cg0Q0eJwZ7202GzIzM9vvL1q0CFVVVWrhsyeeeAKhodrONxENTIilBnGtRWqGXX7giH5vZ3ddEBYXaVV7dwwvduIIvV+g3oYoi9Zj93PrdO1B9kUnD8FjdSLvXlT0xwqtCl0WFNX3fzLZgFSZtBaLUY29S6InR5hgMurR3GpFRbObBk00CPp1Km7IkCEd7suCJVJFds4556jKMvIy9gVFY4b5ZSsX6YVutuoRrLciKajF3cPxeFtDpuPYms+Q3JKHBHM+SgKd18PYoNfh6MxofLujFD/vq8CpCU7bNBH1A+M9kfdWoZcEpKLJcOQ+pj35R24irNDhtIRqHBXJKvTO4ixlqDTGYqV52KEkuszuHED7HCJnYOwmcuCF/dBXVoS2J9HdpSrYXol+oFev1+t1GBIbih3FtTjY1K9aXSKv4LT/dwcHB+PgQfZK9Dqys1/q561c6rVWLpmhzW470+tNGg1h2BU8Qd2WBUadbWxKBMJNRtSbLcitYwAm8jSM90SeLaNZ64eeF9T/Vi7ba0345GBbFfoIVqF3J9ZSpq73NIWiTB8HNJQD5bvdPSyibjF2k99qr0SPhTdotOhUOxdxjDuT6PZK9F62cxGZcdq4ixqZVCHf1a9K9JNOOkn1L7azWq3Ytm0bTjvtNGeOjQZDfanWykVn0BYV9ed+6CFs5dJbm0KPwejGdRjduBbfR54Ls97ktG0b9XpMGxqDr3NKsLPGAF2A87ZNRH3DeE/kZWw2pDshif5krjYVbE5iFbLDm5w2PF8SZGtG88HdCEoagWXh5+KS6he1avS4LHcPjfwcYzdRd5Xo2tpbnm5NZShabHqkmMxq/Q136Ws7F5EZGyrLt6KmRQ9DeLwLR0fkZUn0q666qsN9CdKpqak4+eSTnTUuckcrF6OWTPYnlWYDqlqMMMDm1iDlbQoCh6PcmIDY1hKVSN8UepxTt5+dFIE1+ypR3diC8MlnO3XbRNR7jPdE3iWqtRQRliq0woiCoKH92sauuiB8Vhypbt/GXuiH1bj7J5VE/9IyBZfgRW1x0Unz3T0s8nOM3UQanc0CNFZ5VSW6vR+6VKG7sztYVbCWRA9prUJQSzWaA7T9gsMxBRiQHGlCYXUTgke0LbpN5GP6lUS/8sor1XVJSQny8vJU37X4+L6daSorK8PUqVPxzTfftC80JgufXH311di9ezeuueYaPPzwwx3Oovfkvffew29/+1u0tLTg0Ucfxdy5c/vztfxT2Q6/buWyp60KPTXYrBaJol7S6bA59FicWP2RWmB0U8ixTu0BKr3Rpw+NwdJtxYiYfiHqzVanbZuIBjfeE9Hgt3IpDBoKi65/69z8c0+CWpT0jIRqjArnLL3Dadi1ClHHX47vK6PRZAyAiYuLkgdg7CbSBFqkHYoN0AcAgf1fI8Rdi4q6U4shBHWB8QgzlyK6KR8He5FEF5lxoVoSfdjRLh8jkTv0q+FwTU0NLrjgAiQnJ2PmzJlISkrCRRddpB7vbQL97LPPxr59+9ofa25uVoudTJkyBWvWrFFTzmQl8SORxPvll1+Oe+65B1988QXuvfde7NjRlhimw5PejXKRVi6xI+DP/dCHhfIgsa+2hUxFKwKQ0FKIpJb9Tt/+qKRwhBttMASH45Nd9U7fPhG5Pt4TkXf1Q6/UReHjIq0X+i2sQj+ilpK9iAywoskCrLCOA8p2Huq/S+QmjN1EGlNr2//nQ2K8YtFnaYOyuTpY3T4mxv3HvxXB2iLF0Y37+9TSRZgyxsNsYZEi+Z5+JdFvuukm1VstPz8fjY2N6rq1tVU93huXXXYZ5s2b1+Gxzz77DNXV1XjssccwfPhwLFy4EC+88MIRt/X888+rvm9SuX7UUUfhlltuwWuvvdbj6yVZLzsQjhe/JTv6InqIX7ZyabboUNgUoG6zH3rfNetDsCNkkro9vv5Hp29fr9MhO9Kibn+8sx5ldfwbEQ22gcZ7Iho8OpsV6c27BpRE/ynoaFihw8nxNRgXwV7ovTE6vEVdfxnY1iqD1ejkZozdRJogexI9OAbeYHVlqIrBkptICdZiiztVmTLUdXRjXq/fExcWCJPBBn2gCdtK2S6XfE+/kuiS8H7iiSeQkpKi7sv1448/jk8//bRX73/uuedw2223dXhs48aNmDFjBkJCtBV9x48fr6rRj0Te59jfbdq0aVi7dm2Pr1+0aBEiIyPbL+npWq8nv1SmHWghtv8LT3mzvMZANV05JqAVEQFsF9LfBUbFqIYNCLI2OH37aSFWtWhXQ4sNjy5tO+lDRINmoPGeiAZPQks+TLYmNOmCURKQ1uf3GyISsM2otfe7dRir0HsrO0JLdHxlHgerTaf1RSdyI8Zuom4q0b2sH7on6E8lurRjTjJpuZV1RSyCI9/TryR6RkYGli1b1uExuS/91npj6NCuCx1JRbjj4/Ifn8FgQGVl5WG31fl9ERERKCws7PH1d911l6p4t1/kzLxfaq4Fatt+Jz9t5bKvrR/6EFah99vBgCEoCUiBES0Y0/Cz07cvs+4qv35O3f7vz3nYVujHM0eI3GCg8Z6IBr+VS35QFmy6vu/iR864CFadATNjazEpqtEFI/RNQ0NbERZkRFlLEDbZhrESndyOsZtIE2Txrkr0H8vb+qHHekYSvSq475XoIilYS6KvP8gZbeR7+rWwqJzZnjNnDt555x0MGzYMubm5+PHHHwd0dttoNCIoqGNLEZPJhIaGBkRHR/f6ffb39ERe2/lz/FL5bu06PAUI8o5FNpzJZpMkurbgVmYIpxkNdIHRU6reUwuMrg+d5fSPaD6wFcemm/BjfhP++slWvHXtjF4tOExEA+eKeE9ErpHR1P9+6C36IIQdpcXwW4aVOH1svsyoB2ZmxeGzLQfxjWUiJhZ8ArQ2+2WrRPIMjN1E3leJXm42YHud1g99RrRnVaJHNeUBNivQyxP0CSYbbJZWFNQCeeUNyIjVuk0Q+YI+lalIb7WVK1ciPDwcOTk5OOGEE7B06VJ1vX37drVwSX/FxMSgtLS0w2O1tbUIDAzs0/t68x5yaOUS55+tXErNRjRYDAjQWZESzCT6QOQET4FZF4TY1hKkmnNd8hlXjA9HoFGPVXsq8MXWgy75DCIanHhPRM5nsJmRYt6rbucFZfX5/aUhw6AzBiCt9QCme8BiZt7mpFEJ6vpbTAEszUDRRncPifwQYzfRIZFBQIC1yWsq0Ve1tXIZHdaIuCBtXTB3qzElw6IzIsDajHBz70+wB+iB5oIcdfvbnTwxT76l10n0zZs3Y9SoUTjnnHPwySefIC0tTbVGkR7mDz74II499the9TDvydSpU1XQt9u7d69aBFSS5H153/r165GamtrvcfgFqY6p2qfdjuv7gZYvsFehpwebYWRR84C06E3YHjzZZQuMioRQI66bOUzd/tunOWhq8YwdCyJf5Op4T0TOl2TOgxEW1OkjUGWM79N7myw6VARrawRNN69x0Qh92wmjtN98k2UIymwR7ItOg46xm6ijkbFtqa7AMK+YGeRp/dCFTWdElSmtz33RReMebX/i2x0dC2WJ/CaJfu2116pLSUkJ7rnnnvbHN23apPqWz507F9dcc02/BzJr1izV3/yll15S9xcuXIjZs2ervuiiqqoKFkvXxNmFF16It99+W+041NXV4cknn8Tpp5/e73H4hYo92nQcOSMbEgt/tL+9Hzqr0J1hU+ix6jqrcRMiDa7pMX/jicOREB6E/IpGvLhCq7YjIudzdbwnIudLbatCLwgapi0o0geba4Jh1RthLtmLTEvfDpJJkxhhwtiUCLVg/XLrePZFp0HH2E3U0chYLY+E4J5bA3uSle390D1rNtihvuh9TaKvVdc/5paxAI78M4m+ZcsWXHTRRdDru75FEt3XX3+9CtL9Jb3Nn3/+edxyyy2Ii4vD4sWL8dBDD7U/L33RJVHe2YQJE3D77bfj6KOPVhXoMpabbrqp3+PwC2U7/bqVS7NFh6KmAHU7k4uKOkVpYBqKAjJggAWzI12zWG9okBF/OGO0uv3PZbtxoLLntQ+IqP9cHe+JyPlSm/eo64JAbdZWb7XagA3VWq/SmtUfgJPzBt7SRfqiq0p0WYCHaJAwdhN1NCqu7b8FLygalNzEnoYg6GHDNA/ph25X2dYXva+Li7aU7kNMsB5NLVb8tLfCRaMj8uAkulSF33HHHSgrK+vyXH19Pe677z4cd9xxffpwm82GzMzM9vvnnnuuWvjklVdeUX3cxowZ0+G1EydO7HY7f/vb31Qbl/fee08tmhIcrC3IQF3pbBagItevW7nkNQaqSqGYgFZEBGgrR5PzqtHPiN7vsoPwCyalYlpmDBrMFtzz0Rb17wIROZcr4j0RuY7OZkVyWyV6YR+T6NtrTdoaMZZG1Ocsd9EI/cNJo7WWLlKJ3lpfoc38JBokjN1EHY2yt3Pxgn7oKytC1fW4iEZEelh+otLUv0p0MTlJm/3/7Q72RSc/TKK/+OKLqp1KcnKy6rcmC5OceOKJKrEtleNy9vuFF14Y8ICSkpLUauKxsX07YygJ91NPPZWLih5BmLkYsJiBwFAgPAX+aG9bKxdWoTvXzuBJaNKZkBzYgNnD2qbPOZler8PCX4xDoEGPb3aU4pNNRS75HCJ/NljxnoicI76lAEG2ZjTrTCgL+H/27gNMqvL6H/h3et3Z3ntnF1h6RwQFQVCxK4ot8WdLYqIJMSZqYpK/JJpo7MYSSyyJxi5FQEDaUhaWBbb33mbL9D7zf+69uxRpy+70OZ/nmdx3Z2fufSO7e+eee95zEkf8PuY+9KFB7sI9xtgEOGm59VhMTo1EhFwEDZQ47MqhuujEq+jcTchZaqLL/T+IvqfP/+qhjzUTnTE1kYu7fE910UkQEY70hUyDT6ZJCZMpzjTy7OjogM1mQ0REBHtyZpqV8C6wBiPxvgjzUKmN6NwLrpkZDJgLxuahpqJUD9297HwxKuUzMMWwE/dNF+MdDx0nJy4MDyzKxj+21OLJr8uxIDcW4XKuPA8hZOzofE9IYEm2chnPHeJMuHj8C0oqGLAJIeY7EWXyTCm2UCLg89jPJF+VdbAlXaa37gWm3OrraZEQ4YlzN5PVPmPGDGzbtu346vEHH3wQL7744vHXZGdno66uzu3/fwgZE5fzRBDdzzPRmfhE8VBT0bnR/hhE5zLRVZZOCJwWOPgjb9JaFC+BkM9Dg9qA5j4D0qO5G/eEhEQQ/eQTJfMggSnc3HYiiB6Ceq1Cbtkyz4kkGQXRPVHShQmiX5UvxFcGnceOwzQZ/bqsA/W9BqzdUIm/XFfksWMREqrofE9IYEiynNRU9AIcHORqoU9UmWDrtHtkbqFY0oUNojsnY03Lu76eDglB7jp3MwH0K664Ak1NTac8X1JSgnXr1rFB+eF664T4G5FZDbmIx5Zw5UnD4c9aTWK0m8UQ8lyYEeFfTUUZRlEUzAIlpA49Ikyt6FPkjPi9chEf0zMisbehH9ure3HHXAqik8A38nQVEvAmxfMhdhoBvgiI5JblhJqmoSz0VJkVQkqkdLt+UQKOGaPYO85LRIc9dhyJUHA8cP6fA63Y29DnsWMRQggh/st1Sib6SHWZhegwi9kmZpPDqVG3uzCZ6MzHywpXBrp6ewAjNVMjgenmm2/GLbfccspzdrsd5eXlWLBgAZvhzjzCwsJ8NkdCzkaq51ZXWQRhAN+/b/TsGcpCnxJuhFzoh/2+eDwMDmWjR5ovvKTLwqGm21QXnQQLCqKHkBV5QwsPmAA6/4IXIQSF5qF66FTKxXM2DHA3aC4THwLP5bnMthkZUbhlFndC/82nR2C0UhYdIYSQ0BLP10Lh1MEOAbrE3DlxJA5ruCz0/DAzlEL/amIWyKKVEkxKjWDH3zsmAW0HfD0lQkbljTfeYEu3nOzo0aNwOp1seRiZTIZly5ahpeXsQTWLxQKtVnvKgxBvkOi5n0uzUAV/VNvRj0M17exjXRMXkstEx/HnRvroUGu9XBf9wpuLLhoKou+p74PZRr1XSOALzUhqiFqRO/TPHRWay/PNDh46zVztbGoq6jm7dYm403AQsQodMgf2oCFqgceO9ciycdhW1YOmPiP+uqEKT66c4LFjEUIIIf4mT9TFbrvFaXDwRtYfxGDno1YvZceUhT52u3fvPuXraAuTsCFjS7qM3/AWyg6oR7SfrKwszJkzx0OzJOTCZGaevrKloqKCbVrK1ERnmpU+9NBDuOeee7Bx48Yz7mPt2rV48sknvTBbQk4l1XMlbC1+FkRXa7hyLT97ZTMA5gGk/OTfECiBF155C8+0HhvVfk0eTiYbros+muaiefFKJIZL0akxs6vHhzPTCQlUFEQPEQKLBrNTBCEdRG8xidm6aFEiO1QiyrryFLtLgLcP2/DreRJM7Prco0H0cJkIf72uCLf/az/eLW7GZeMTMC8nxmPHI4QQQvxJnrCb3baLR/7Z7phWBid4SJRaESehVVyjpe4bYLevvvoq+xgmTshB4h3/wC7nBNxQ+hxWv/vJCPfIw549uymQTvzWrbfeyj6GvfLKK2ywnckwV6lOD1Y++uijePjhh49/zbwuNTXVa/MloUsyVM7F3zLRdSZuNfziq29A0fgCaF1SbHZEgg8n7rvtOgh4117Q/rbt2IPSnVtgsTv8NhOdaWi8MD8WH+1vZeuiUxCdBDoKooeI8J794PN4MAojIZf618nEW5qGSrlQFrrnvX7QygbRMwd2I8zcCZ000WPHWpAXi9Wz0/D+3has+aQMGx9aAJV0ZNl4hBBCSDBkordLRlYP3eECjmpl7HiSyuTRuQU7nV7PbictWoFJ4/OPP89UtN3nMkPPk0OaOh53/XQWHDh3Td6m5lbs+PoTNDQ0UBCdBIy4uDi2vEtnZ+cZg+gSiYR9EOJt0uPlXPyzqWhUTCxS0tNRppEBaiBZZkN60shLsg1TRlTCGwako89EZzCBcy6IztRFH+/m2RHiXRREDxGqnr3sVitJAlcFM7S4XEw9dK6pKNVD97z6ARcO2zMxWdiIou7PsDv9Jx493qOXF2BnrRrNfUb86esKPHPDJI8ejxBCCPG1eAUPCQItu8quc4RNResNEhgcAsgFDuQozR6fYyiIT0zA5KJTgwLd3S5U64Fi53gsGReOLjGXxXcuOzw4R0LcYc2aNZgyZcrxhqPFxcXg8/mUXU78i0UPianbr4Pow1pNXHwiVebf8Ynhci4yuwZS2yDMIq73x0gxK8VFAh5bgrVJbUBGjMJDMyXE86ixaChw2KHq2c8ONdIUhKJeqxBGhwAinhNJfn6SChbrrdPY7YSuLyBwejb7XyER4m83TGKah+OTg23YUsF9cCKEEEKC1UXpXHZzrygRFj6XXX4+ZUMNRSeoTBDwPDq9kJY29Flzp7MISZZGX0+HELeYNGkSHnvsMXz33XfYtGkT7rvvPtx+++2Qy0MxRYv4LXUNu+nSO+Hg++9KCKcLaAuQILpdIINOHDfqbHSlRIjp6VHsmMtGJyRwURA9FLTth9CmR7/JBYMoJqRLuTAnKCFdNHrFfnsetJIEyO2DyO/d5PHjzciIwj0XZbHj33x2FP0G//4wQgghhIzF/DQuiN4u5s5959NrEaLDLAYfLkykUi4elTa06vGIKxMKM1dyh5BAt3r1atx000247rrrsGrVKixbtgwvvfSSr6dFyKl6q9lNZa9/90BTW4WwOPkQ85wB0Z/keHNR8+hKuiwaF8tut1X3unVehHgblXMJBTXfspuNdXaMywrN+yZNQ6VcMqiUi9c4wUdZwvW4qPklTO78GBVxVzCdRUa1r/Xr16Oy8vw13xKdQJwkDD16C25/YR1uTjUeP2RjI2WCEUIICR4XpXEf4ztGGEQ/wtReBZCtsEAp9O/gQqBj/vsmiAzosinQZJZzdQVH+RmIEF9yMT+7J1m7di37IMRvqbkgeoXaiTkBUMolSWYDPwBOD0xz0TRNyaiaiw7XRX9qfRX2NvTBbHNAKjp3rxBC/BUF0UNBLZcFvK7WhnGXIeSYHTx0mblGk+nUVNSrjsWvxJzWNxBvqEKi7gg6VRdWq7yrtYndPv744yN+jzg+Gwm3/R3HNGLc++9/wFh5apXR7p5upORSQxNCCCGBS+gwYVI8lxjRLska0WehKv1QQ9Fwo8fnR4BkuRNdGmC/IwcFDjUGhVwWHiGEEG9kojsCIoju76VcTstEH2UQPTdOiaRwKTo0ZhQ39GFRPlcehpBAQ0H0YDfYCvRUwAU+NtY58EuEnhaTmG26FSWyQyWizCtvYpqOVMUsxYSer9hs9AsNomsG+tjttCUrMT4/e8Tva3K0oFmQhcQrH8L0xXMhcVlxtOwISndugVaru+D/H4QQQog/iTU3QMDnoccRBoPg/I3TqvVS2F08RIttSJLavDLHUJcqt+OgBtjhKMKl5l0YVFIQnRBCPK63it1U+HE5Fyd46DCJAiqI3i/LYLdRRi7J7ULxeDwsHBeHD/e1YHtVDwXRScCiIHqwq+VKuRgiC9BvKkYo10PPoCx0nziceCMbRM/t+w47LL+AQXLhF5FJycmYXDTy7PGJLuDjNht6rCJ0xczAVQmD6OvpRukFH5kQQgjxP3GmenZbbUsY0evLtbLjDUWpqoh3JEutEMGOLkTDaR4ElL6eESGEBDmbGRjggryVav8NohuE4bC5+JDynYgR+389dEa/nFv1FmFuAd9ph5N/4aHEhXmxXBC9huqik8AVmgWyQ0kNV8pFEz8boYgp49c8VA89neqh+0SvMh/tqskQuBwo6v7MK8cU8IDL4jUQwMXeRKnQSb1yXEIIIcQbYs117LbWHn/e1/ZYhOi1ithzYr7S7IXZEYaQD2RJtOy4waTw9XQIIST49dUBLifsIiW69KfW8/cnWlE0u02RWQPmxrZOHA8rX85e00eYW0e1j7k5MRAJeGjuM6JRbXD7HAnxBspED2Y2E9DI1YPWhmgQvdcqhNEhgIjnRFKALJUK1mz0ZO1hTOz6DPtT7oKDz93Y8KRosQOzo/TY3R+GHeowZAjlHj8mIYQQ4nF2C2LMTccz0RNGmIWerbRAJvDfoEIwSlAA1RZgvz0H8x2D0AsifD0lQggJ+qaiZmU603YbgRBEDxg8HvrlGUjQVyDK2Ih+eeZ531JZWXnacwXRIhzpseLfWw7hiryR32COiYlBWhpXl50QX6IgejBr3AnYTYAqGSbVyOtJB2MpF6bWmDBA7vIGo7qoRdCJ4xBm7UGueguq4pZ75bhTI4xoMEjQaRGjPWYWgDe9clxCCCHEYzpKIXDZ0a13ossZfs4gut3J1UNnFIaZvDZFwkmRO4F+YJ+zANeat0OvmOzrKRFCSNA3FTWHMUF0Py1lKxBBJ4wMqHrow/rkmWwQPdrUgDpcctbXafu5ci2rV68+7XthM65B1CU/xsufbcPvP/n9iI8tk8tRVVlJgXTicxRED4F66Mhdwt45DEVNQ6VcMqiUi08xNdOOJFyLeS2vsQ1GvRVE5/OAJXFafNgWDYMsAcopl3vluIQQQojHtHCBgV0tDiD+3J/v6g1SWJx8hAkdSAuwi/VgwKyKi+QZMOBSQGM0AVTVhRBCPB9EZzPR/ZMkeRxcPAEUAgciRQ4Ekj4Zl5jJZKKfi0nPlTJbce/vkF807ZTvaW3A5k5AkTUVD774GVv67Hy6W+rxwV/XQK1WUxCd+BwF0YO5GPhQPXTkLgWMCDlmBw9dZq7rdTo1FfW5o/HXYFbrW0jUlyNeV47usJE3Ch2LSLED86J1+F6tQuTCH8GC0dVwI4QQQvxC81AQvZUJop/7peW6E1noI8mnaO3V4lBN+5im16HmLp4Jl8OSK9Vgv0mBOpMKXO4hIYQQz2ei+ydp+qSAq4c+bLiES7SxYUSvj05KR0ruqdf8LpcLeweaoDPb4YrOQEoM3V0mgYWC6MGqtwrQtAACCZB1MXCMO6GEkhaTGC7wECWyQyXy3+7cocIkjkJNzGUo7F2HKZ3/xcawP3rt2JNUJpS0mmCQxaPVmQynywV+oH1qIYQQQpxOoHUvO9zZbMf46Wd/qdbGR6uJKWvnOm8pF6Nex26f/mQf+3AHk9Xulv0EugSFADABB+xZuMqhh1mg9PWUCCEk+DjsXGNRfw+ipxUFZCmX4XIujEhTM3guO1y8Cw8n8ng8ZEQrcLRdg6Y+AzIpiE4CDAXRg1XNUCmXjPmAWBHS9dAzKAvdrxqMMkH0PPVm7Mh4EEZxjFeOy8TLk9X7UBW3GEaJHGWtg5iSRvlghBBCAkxPBWDWwMaT4HCXFuda01Wukx2/UD9fMoHVYma3s5etxNxpXJbcaG3bsQelO7fAYg+sZeqeEq/kAWqg0pWBW007YFbm+3pKhBASfAYaAacNEMlhlcXBH1l5YkiS8gOvqegQrSQRNr4EIqcF4eZ2DMpGd7MiI1rOBtGb+4xsZjoTWCckUFAQPVjVDpVyyVuKUORiVjsfr4dOQXR/0R1WiI6wiUjSHcWkrk9RnHav144tthswsO0tRC/7GYob+pAdp4RKypX7IYQQQgKpHrpalgmHi2vcdSZOF1AxFEQffwENRVVRMUhJH1sGnzKickzvDzZygQvZgh7UO+LQbbCDT4nohBDisVIuiMkFeCMotO0DXaJk8PgCSBwGhAfiSnkeH/2yTMQbqhBtbBx1ED0lUg4BjweNyYYBow1RCi5uQ0gg8M+/LmRsTANAC7fUF7mXIRQZBSoYHQKIeE4kyWy+ng45SWnSKnZb1Pk/CBxc5pu36Ms2Qe40wuZwYXt1L3vnmxBCCAkYQ0H0HmnOOV/WbhJBbxdAwnciW0HJBL6WI+XqxNeaI3w9FUIICd5ytozYcfBXHaJUdquy9SFQDddFP19z0XMRC/lIieRu9Deo9W6bGyHeQEH0YFS/FXA5gJg8IIr7IxdqBsWxx5cwC2h1kF+pjV4EjSQRcvsgCnvXe/noLqTYO8DnAY1qA+p66aRNCCEkQDA3foeaivbKss/50mo9d3GaqzRDSJ/2fS5WyWXZldizIPRyAgEhhIQEdQ23ZWIgfqpdzAXRw21qBKrhuujRxvox7Sczlis53NBrcMu8CPEW+lgdjGo3h3QWOmNQxAXRM+SBV2ss2DENSEqTbmbHUzs+BFzeXcomhRXT06PY8ffVvVSzlRBCSGAYbAZ0HQBfBLX07EkSdidQa+D6wuQrKWDrDyIUEshggRoRgOHsZXgIIYQEZyZ6r0WIASHXD0xl60eg6pNzN/FjjA1j2k/WUEPRTo0ZRmpETgIIBdGDjdN5IogeovXQ+VIl9EKuaWQ61UP3S8fiVsIsUCLK1IzMgd1eP/6MjEhEyEQwWB3YUxe4y+kIIYSEkKEsdCRNhoMvPmdjdauTD6XAgWQplbTzB8yqyImiNnbcaaRScoQQ4vYYiLqWG8f6Z/PmPf1cQwxrdz1ErsBN9FPLuXJyUaZG8J2jD36HSUWIC+Nu+DMrxAkJFBREDzYdhwCjGpCogLQ5CEXSjCkAj4dokR2qQGzYEQJsQgWOJVzDjqe1f+D14wsFfFwyjuvafqRdgy4NZeoRQggJjHroSJt9zpdV66XsNj/MzHwcIn4iS8oFCarN3Go4QgghbqJpBWxGdqUWIv2znO3uPi6IbmouQyDTShJhESggcNkRaWpySzY6lXQhgYSC6MGm5ltum70IEIgQimRZ09gtZaH7t9LEm+DgCZCqPYg4/dDyOy9KjZKjICGMHX9fQ01GCSGEBEoQfe5ZX2Jx8tBopFIu/ig6jLu5cdSRCqeDVggQQojb9FZz2+gcQCCEv2EuM4eD6OamwwhoPN5JJV3qxrSrrFjuv0lLvxE2ByU/ksBAQfRgUzsURM8NzVIurpOC6BkURPdrekk8amKWsOOp7e/7ZA7zcmIgEvDQpTWjukvnkzkQQggh52VQn2iado5M9Hq9BA4XD1EiO2LEVGPUnwgkKsTxBmCFGBbdgK+nQwghwUNd7delXFpMYrSbxeC7HLC0lSPQDZd0iTGMLYgeoxQjTCqE3elCa7/RTbMjxLMoiB5MdF1A59DyoFwuOBlq+oRxECgiwXfZkSSjLB9/dzBpNbvNV29BmKXL68dXSISYkcEtq95Vr4aV6cZGCCGE+GsWemwBID97ORAq5eK/eHweJoo62HE31UUnhBD36any6yD6rqEs9DhbF1y2wE/061UMBdGN9WPaD4/HO17SpZ5KupAAQUH0YFK7idsmTQWUXL3nUNMqTme34TY128SJ+LdeZT5awqeDDwem+qA2OmNKagRUUiEMFgcONlNmGCGEED/Usve8WegGOx+tJq7hKJVy8U9pMi54UmOJ9PVUCCEkePQMZXfHj4c/2jMURE+ytSAY9B3PRB9q5uqGki5Mc1EnlVclAYCC6MFYDz0vNEu5MFrFGew23Nrr66mQETqQcie7ndj9BaS2QZ80Gb0oN5YdH2wZgNZEKxgIIYT4meY93Db97PXQa/QSuMBDgsSKcJHDe3MjI6ZScsGCKmcyHDarr6dDCCGBz+kAeiq5cfwE+BunC9jTPxREt7YiGKiHMtFV1m5I7GMriZocIYNYyIfJ5kCXhhIAiP+jIHqwsFuAhu3cOPcyhKJBqwC9wgR2HGGjIHqgaAmfiW7FOIicZkzp/K9P5pAdq0BKpAwOpws769Q+mQMhhBByRhb9iXJ9aXPO+rJqvex4KRfip6ThyOR1wQU+9HqNr2dDCCGBr78BsJsBkRyI5BLq/EmFTooBmxAKgQNx9m4EA4swDFpxvFvqogv4PGREy9lxg5pKuhD/R0H0YMpQsuoBRRyQOBmhaEefEi4eH9beZkicdAEZMHi849nokzs/hsju/ZMnU4/t4rxYMBWA6nr06Bg0eX0OhBBCyBm1HQBcDiA8FYhIPeNLNDY+ui0i8OBCriLw660Gs/FirgdMp5HqDhJCyJh1H+O2cQUAXwB/LeUyK8oAPoKn/5Zakctuo41jC6IzsodKujT06se8L0I8jYLowVYPnclC54fmP+t2dRi7NTWU+Hoq5ALVRS9EvzQNUrsWE7s/98kcYpQSFCap2PGe+j64qCYbIYSQAKmHXmfgGoomy6xQCIPnIj0YJcvt7LbKypWSI4QQMgbd/l0PfVcfF6OYFxVcAWK1PJvdxrqhLnp6tBx8HjBgtKFPT4kAxL+FZrQ1qOuhh2YpF6bW2I6hILq54aCvp0MukIsnQEnKHex4WseHEDh9Uyd0VmYUu6SsfdCEln6jT+ZACCGEnKJlz3lLudTpJew2h7LQ/Z5CoWKzEZudcbBYqS46IYS4J4juf/XQzQ4e9g0o2PH86LHVDvc3vYo8dhtrqBnzviRCAdKjuf9ONT3BdbOBBB8KogeDvnqgvx7gi4CsRQhF5VoZ1FYRRE4rzG0Vvp4OGYXK2MuhE8dBae1FQc96n8whTCpCUXI4O6ZsdEIIIT7nsAFtJedsKqq18dFlETO3pCmIHgAcknCM43PN5bS64AqqEEKIz8q5+GEm+v4BBSxOPtvwO08ZXOfnHmU+u4011oHn4lZYjUVeHFfSpbZbR9fgxK/5VRD9nXfeYWsT//DBPH8uRUVFp7z+7rvvRkhmoafPAaRcOYpQs20oCz3J1go4x/5HnHifky/CwaRb2fH09vfAY+q/+sD0jEiIBDz06Cyoo7pshBBCfIlpKGozArJIIIa7YP2h+qFSLklSG5VyCRCF4l5222ES+noqhBASuMwaYLCFG8cVwt8Mr5RfEKNn2oAFlUFpKqx8OYROC6KMTWPeX2asgl0RzpZ0MdAqLeK//CqIfsstt2BgYOD4o7W1FTExMbjooovO+h6j0Yj6+nr09PQcf9+LL76IkFI7FETPXYpQta2XO0GlWht9PRUyBscSroZJGI5Icyvy1Ft8Mge5WIgpaZHsuLi+D066E04IIcSXjeMZqbPP2vOm1sCVcslVUFP1QJEk5252lFvjQB8zCCFklHoqua0qGZBHwd98fzyIHoSrjnh89Cq5ki5xhmr3lHSJkrPjmu4g/O9FgoZfBdHFYjEiIiKOP9577z1cc801yM7mmhacSWlpKZuJHhsbe/x9MpkMIcOiA5p2c+O80Ayi91kFOKzh/uCmWpt9PR0yBjaBHIeSbmHHs1rf8lkH86lpEZAK+eyd8KpOOokTQgjxcVNRZrXhGVh4YnSaRew4O8iWigcziTISYljR64qAwUIZd4QQEmylXDpMItQapODDhflB1lR0WI+CWyEXpx97EJ2RF8/ddKjt1lNJF+K3/HYNodlsxvPPP499+/ad83X79+9HW1sbG0S32WxYtWoV/vGPf0Ai4bJyfshisbCPYVqtFgGtYTvgtAGRmUB0DkIRs0zKBR4KwkxQUPmNgHc48UZMa38f0aZGzJXW42sfzIG5Ez49Iwq76tTY29iH/IQwdnkZIYQQ4jVOJ9BSzI3TzlwPXS2IZdLBkCixIoxKuQQMq0iFIkETShx50OoNvp4OIYQEeFNR/wui7+zjanxPCjciQuybMqWe1qMcx27jDFVu2V9mDFfSZdBkg1pvRWzYmWN6Y9XS0gK1Wu2WfTGVM9LS0tyyLxIY/DaI/uGHH2LWrFnIyMg45+uqq6sxf/58/OEPf8Dg4CBuvfVWPPfcc/jNb35zxtevXbsWTz75JIKuHjqThR5shbYusJTLohgdrA2+ng0ZK6tQiUNJqzC39XWsUuzHb300j0kp4ShtGYDObEdllxYTkriGo4QQQohXqGsAUz8glAGJk874kl5hHLvNoSz0gJMv7keJCWgxicFVtSeEEDK6IPoE+G8pl+BN8hvORI811AAuJ1viZSzEQj4youWo7zWwJV08EURnAujjCgpgMhrdsj+ZXI6qykoKpIcQvw2iv/baa2xgfCSvO9kTTzyBF1544axB9EcffRQPP/zwKZnoqampCEjMEpfazdw49zKEIrvzxAlqUawWQ7cUSIA7nHQzpnZ8iHT045oCIWw+mINQwMfU9EjsrFWjpGkAhQkq8CkbnRBCiLe0DNVDT5kOCMWnfZsvj4CGH8GOc6geesBJVAAwARXWBEzB2JuyEUJIyK3W6q7wy0x0JkaxaygT/eJgrIc+pF+WCTtPDInDgHBzOzSyVLeUdGGC6LU9eszNjgbPzYmiTAY6E0C/9ZFnEJ929rLRI9HdUo8P/rqG3ScF0UOHXwbR6+rq2MeSJUsu+L1xcXFob28/6/eZMi9nK/UScDrLAH0XIFIAGfMRipha6Bq7EOFCO6aEGymIHiQswjA2kD679U08sUCCJyy+qYk2MTkcB5r6oTHZUNOjw7gElU/mQQghJITroaeduR66PHc2uwoxTmKDSkSlXAKOMg5haiN0kIMn8MtLMkII8V+aFsCqAwRivytrW6aVQzsUo2DKuQQrJ18ItSIHCfoKxBuq3BJEZ0q6CPk89vq7R2dBvMoza7WYAHpKrn/dfCGBwa8aiw77+OOPccUVV0Ak4holncucOXPQ2tp6/Ovi4mKkp6cjJNRu4rZZCwFhkNwYuEBbe1XHl0kJ/fKnmYwWU9LF6BRhUoIAM2VtPpmDSMDHlNRIdsxko1ODE0JGh8nQyMzMRFPTiWzLY8eOYcaMGYiMjMSaNWvo94uQH2ouPmdTUfm4eew2l7LQA5JNoMBkIfc3kS86faUBIYSQEZRyic0HBOePG3nT8Er5+dF6CIJ8IfOJ5qJVbrv+ZgLpww1GCfE3fhl23LhxIxYuXHjKc0y9c4fj9IYM48ePx7333ss2IH333Xfx97//Hffffz9CwvF66KFZyoWxbegEdUlsgDeIJaexCFX4ysjVgF0VcYQrX+Sj2uhiAR99Biu7tIwQcuEBdObG+MkBdKbB95VXXolp06ahpKQEFRUVeOedd3w6T0L8iqaNy7LjCYCUGad92+TgQZpWxI6pHnrgypZwn1/VghhfT4UQQgKLH9dD36EO/lIuw7qVBew2Xj9UWscNcuO5/37MSnBKsiH+xu+C6CaTiQ2Iz50795TnmUy1o0ePnvb6v/3tb2x5lkWLFuH3v/89nnnmGdxxxx0IegY10H4wpOuhd5pFqNTJwIMLC0LgBBWKvjRMhtbiQpZ4ADnmIz6Zg0QkwKRUrqkoU9qFTuSEXJibb74Zt9xyyynPbdiwARqNBs8++yyys7Px1FNP4a233vLZHAnx2yz0hImAhEsYOFm1VggeXwC5U48I0elJJiQwRCm5laQ1SAPPzzIpCSHEr3Uf88t66INWAY5o5Oz4oiBuKjqsK4y7iRGvrwTP5Z7PI5nRCjaJTWe2o33Q5JZ9EuIufleATyaTsRlqP3S2wFVERAQ+//xzhBy2oaiLu7hSJSEUbe/lLionhxsRLaYLyGCkc8nw3F4rfn+xBHO1G1AvnQjXGLt+j8bk1AiUtgyyddma+43IiOaWmBFCzu+NN95gS7n8/Oc/P/5cWVkZZs+eDbmcu8goKipis9HPhvlccPJnA6YpOCFBrWW4lMupSSXDKrVcwDXGrmY+PXtzZsSNnPI4xGIQvYhA4cRCX0+HEEICMBPdv4Lou/qVcIKHPKUZiVIbgl2fPBM2vpRtLhplakKffGzNOhlCAZ/NRi/v0KKyU4cCusdM/IjfZaKTEaodKuWSuxShautQKZdFsZSFHsyeLbZA5xAj2t6Ncaah1RdeJhcL2SajjAON/T6ZAyGBigmg/xATBD/5eR6PB4FAgIGBgTPuY+3atQgPDz/+SE0de+MiQgIiiH6GpqIWuwM1eu6KMtrR6+2ZETdy8YWYJOJ6O6UWTPb1dAghJDBYjUBfvV+Wcxmuhx4KpVwYLp7wREkX3dCNDTcoSOB639X26GCn3unEj1AQPRA5bEDdd9w4bxlCkcXJw+4+rlbWJRRED2paC/CplsswmK39Fnw3LRO7UFPTI8HnAR0aMzo1tKyMkLEQCoVsKbaTSaVSGI3GM77+0UcfZcu/DD9ObihOSNAx9gM9FWcNou+p74PVyYNd14cwJ30GCnRpMq4xrCWB6wNDCCHkPHoruVX5ilhAGQd/4XQB24ZWyy8MkSD6ySVdEt0YRE+KkCJcJoLN4UKHicKWxH/QT2OgZidZtIA8BkieilC0v18Bo0OAWLENhWEU0Ax263T5MPDDEOHow3jjPp/MQSkRIj+B+1B0qGXQJ3MgJFhERUWht/fUDFqdTgexWHzG1zMBd5VKdcqDkKDVOnSei84BlLGnfXtzRTe7NdXtB8/bcyNup1Jyny2axdmw2YJ/6T8hhARrKZfDGjnUVhHChA7MiDQgVHQpuX+HBP1QnXo3YFapjhu69m7SU9iS+A/6aQxENcOlXC4D+AKEom1Dy6QWxurY7GAS3CwuIfaHLWbHs3SbIXD55iJzaloku63v0cNg98kUCAkKM2bMQHHxULkKAI2NjWzNcya4TkjIO0cpF6fThS1DQXRj7YnfIRK4XNIIpKIbDgjQN8DUuCeEEHJOnUf8spTLd0NZ6AtidBDzz9zTLxh1hnFB9BhDPYQObnWVOxQkckkzvRY+hOHxbtsvIUHVWJRcQBA9L3TroW/v5f6gXhJDzeVCxVHFXEzTb4PKMYgiwx6UKi/2+hxilBKkRcnR0m9ErTY0b2AR4g4LFixg66K//fbbuOuuu/DUU09h8eLFbF10QkJe89mbih5p17BNriV8F8wtTBBhoffnR9yLx0MOWtCKeLToqPArIYScV2cZt030Xi+J2o5+xNa0n/M137RlsNscRzMO1XSc9v0Otf/HLnRaLTo7Oy/sTS4XtIJIqBwDELTsxuCge1ZtM+Vchq+9lUWXuWWfhIwVBdEDDdNAo68W4AuB7EUIRU0GMRqMEgh5LsyL1vt6OsRLHDwh9oUtxZLB/2KmbguOyWfDxj+1prI3TE2LYE/kTQY++BKF149PSLDURH/zzTexatUqrFmzBnw+H9u3b/f1tAjxPZsJ6Cg9ayb6pvIudpsbZkONg5ZEBYswu4a9Kjtmjvb1VAghxL85HUDXUW6c6PleEmoNV5blZ69sZgqqnfV1AlUsUu6/HC6nA7967C9wms9eE91k9b/zt9nG9R0rKSlBaVXDBb//4iIxVsQBHdvfxdYSruDF2XodXYgJSSr22ltRtAR2pug8IT5GQfRAzUJnspOk4QjlUi5MnTGViDJ2QkmFfAam675DpEONyYYdOBC2xOtzYO6GRyvF6NNboZx8udePT0igcrlO/eB71VVXob6+HgcPHsTs2bMRHU3BI0LQVgI4bYAyAYjkMtrOVA+9IMyGdT6YHvEMo9UFntCJemcCejpaEJeU5uspEUKIf1LXAnYTIFZyvUM8TGeystvFV9+AovEFZ31dnTMOZU4glm/A9b986Iyv2bZjD0p3boHFzgWs/Yl1aE6TsuMwd8aUC34/z24G7N24aRywQ5+CnbWAxcr9txuLrFglu/oOyigc7LRg5pj3SMjYUBA90NRs5LZ5yxCqtg6VcllEpVxCjpMnwF7VMlw+8D6m67bhiGI+LHyZV+fANDlhaqMzgYywaVeyHcMJIaOTkJCAFStW+HoahPhfPfT0OWyZj5M1qQ2o7dFDwOchX+V/WWxk9GwuATLszWgUZqJ4/z6svJqC6IQQckadh7ltwkSA770Wf1ExsUhJTz/r9w90RAAmYFwUDymRZ36dMqIS/k4hFSExmou3XAidpQBQb0EWWqGSZbttPsxnnnSlEzVaAb6tN+J+t+2ZkNGhxqKBxKwFmveEdBDdaOdhXz9XQuOS2LMvkSLBq1o2BWphAqQuE6bqt/lkDvnxYZAKXBCGRWNXq8kncyCEEBLMTUXnnjULfXZWFGQCuoEbbMJ6uUZ5u2p7fD0VQggJgHroni/lMlIWJw9tJjE7zlRYEIq6xWmwQwiFU48kMVcCx10yFQ64XE4c7rKgoZfK+RLfoiB6IGnYxi3xZZYtRbvv7l4g2d0fBquLjxSZFdkheoIKdS4eH3tUXBmVqfrvIXd4/2YKc0c8W8ktefu6xnBamQpCCCHkgjE1zlv3n8hEP0sQfUlBvLdnRrygvYqrhb+nPwwuu83X0yGEEP/kh0H0FqMYTvAQIbIjUuR/pVq81b+sW5zKjsfL+t26b6UIMNWXsON39zS5dd+EXCgKogdiPfTcpQhVW3q4pUWLY7U/XOVMQki9dCK6RGkQu6yYqTt7gxdPylI64bSZ0TRox4GmAZ/MgRBCSBDpPgpY9YBEBcQVnvKtPr0FJc3cReniQgqiB6Oyw0chgh3trmi0VOz19XQIIcT/OJ1A5xG/C6I3GiTsNlNuCekYRbs4i90Wyt0bRGfoSr5kt/872AatmW40E9+hIHognTBqN3HjvNAMojPNmL/r5ZqKLo6jeughjcfDLhVXR7nIsAcqu/tP1OcjFgCG8u3s+J09jV4/PiGEkCDTPFTKJXUWwBec8q3vqnrYz0GFiSqkRMp9Mz/iUQ6rBQWiLna8+9BQzV9CCCEnDDQCVh0glAIx+fAHzLm5ycgF0bNCfKV8u2QoiO7mTHSGubkMKSohDFYH/lfS5vb9EzJSFEQPFB2lgKGXy05KO32Jbyg4rJFDbRUhTOjAzEj31tkigadVmodmSR4EcGC2bqjhrpfpDn7Nbr8t70bHINVGJ4QQMgbNu89fyoWy0INazlAAZk8zfaYghJAzxkQY8RMAgRD+oMssgsnJh4TvRKI0tDOkO8UZcIGHZIkBcQr3p+SvyOGSCP61uxF2h9Pt+ydkJCiIHihqhoKE2ZcAQq5pRaiWcrk4Rgcxn2pQE2D3UDZ6obEE0bZOrx/fpm7G+FgxHE4XPtjX7PXjE0IICRJMb43h5vHp80/5lsnqwM7aXnZ82XgKogez+PBwdltsSoGzn1a5EULIGYPoflTKpWEoCz1dboEghEu5MCx8OdTCBHY8P+3UFXXusDBDjmiFGG0DJnx9pMPt+ydkJCiIHmhB9BAt5cI4Xsollkq5kBNdwGulReDBhbnaDT6Zw4pc7o74R/tbYbaFZiMZQgghY9RbBZj6AaEMSJpyyrd21alhtjmRHCFjy7mQ4JWgFELGs6EP4agu2ebr6ZAQp1arkZmZiaamE438jh07hhkzZiAyMhJr1qyBi7kBSIi3tB/itsnT4A+YH/86PRdEzw7xUi4/LOlycbr7g+gSIQ8/mp/Jjl/dXg8nU0uHEC+jIHog0HYAXUwDDR6QswShiOl4Xa2XQcBzYWGMztfTIX5kj2o52w09x3wUCVbvd+uekSRFUrgU/QYrvi6jO+KEEEJGoWkXt02dcdqKw80VXcdLufBCuWNZCBDygZkxXDmA3eWUiU58G0C/4oorTgmgWywWXHnllZg2bRpKSkpQUVGBd955x6fzJCHE6QA6y/wqiN5rFUJjF0LIc7FNRQnQKsllt5dkeqbczurZ6VBKhKjp1mNrVY9HjkHIufhHISlybsMNRVOmA8pYhKItQ1noMyIMiBBTti85oV8Ujwr5TEww7sN8zTr8L+YBtvGotwj4PKyek46nN1bj3eImXD8thYIchBBCRlkP/dRSLky5sO8quYtEqoceGuYVpOL73j4Uq6W426IHJEpfT4mEoJtvvhm33HIL9u3bd/y5DRs2QKPR4Nlnn4VcLsdTTz2Fn/zkJ7jrrrvOuA8m6M48hmm1tJqYnF1LSwt78+ZspNpGFNoMcAhkKGvVAW1DWelnUFlZCW+o1UvZbYbcAhGlp7LaxDlss9UJcQIIOzrR2Tn2kqsn/1yEy0S4dXYa/vl9A17YWotLC+Lo2pt4FQXRA0HNtwj1Ui7D9dAXx9GHL3K6vWFLMc5YglRrHdIs1WiRjvPq8W+ekYbnt9TiWLsWh1oGMC09yqvHJ4QQEiT10DPmnfKt0pYB9BmsUEmFmJlJ55ZQMLdoHLBjN/Y58mCv2w7h+Ct8PSUSgt544w22lMvPf/7z48+VlZVh9uzZbACdUVRUxGajn83atWvx5JNPemW+JPAD6AUF42A0nr2p8l2TRfjXShl21OlwyfSZI9qvTq+HJ0/dtQaulEuOwuyx4wQas0CBerMKuTItwtt34PXXucboY+HQ97Pb4YD8/12UhX8XN+NImwabKrqxdDxXh50Qb6Agur+zmYGG7dw4bxlCkcYmwL4BLgtnCQXRyRnohJEoU8zHNMP3mK9dhw8leQDPe+kAUQoxVk5OwsclbXh7dxMF0QkhhIxcXz2g7wYEYiB5+inf2lzBXXwuGhcHkYDS3EJBYVI4IoQ2DNrlKDu8F9MoiE58gAmg/xCTSX7y80z2p0AgwMDAAFsj/YceffRRPPzww6e8PzU11YOzJoGKyTRmAujv//ZGFKSdeeV9qmYfYKxB4YQiHHzt3OVc1u+vweP/2gyz2XPBbTVTysUmZMvNZiqsHjtOIDqojWKD6NdkWxGbNGPM+6uqrsXXZcDg4CD7dYxSgrvmZeDlbfV4dlMNFhfEs6vDCfEGCqL7u8YdgM0IqJKB+AkIRdvVYXC4eMhVmJEupxMUObMDYYsx0bgX8bY25JqPoFY22avHv2NuBhtE33isC91aM+JV3PI+Qggh5Jyah+qhMwF0kfSMQXTmApGEBj6fhzmpEmxodGJPfT+mOZ3Mk76eFiEQCoWQSLjM22FSqRRGo/GMQXTmtT98PSHnwgTQp+Yln/mbBzXsJj49D/GxZ3nNkMqWXnha7VBDUaaUi5hPDS5PdlAXhZvjmzAjTI366LE3RO9Wnn5dfc9F2XivuBnV3Tq2L9nVU879M0GIu9AnMn9Xve5EFnqI1nqiUi5kJEwCJQ4qF7Ljudr14Lm8Wzt/fFI4ZmZEwe504YO9zV49NiGEkAB2llIu9b16NKgNEAl4WJgfmj1xQtXcCXnsdo85HegaaqRHiI9FRUWht/fU4KROp4NYfGozZELczmkHDEM/e2GJvp7NUCkXLrCbo6CGoj90RB8Jm8OFRJEBKnufR44RLhfhvouz2fEz31bDbKO+ecQ7KBPdnzmdcFR8AwFzp1OYD92hszfPOB9vNddwN6uTx2aiMyiITs6HCaJPMuxClL0X440HcEwx2+vZ6Pub+vHh/hb85JIcSITMby8hhBByjivxpuGmovPOmIU+OysaYVKRL2ZHfGRuHrPyoAoHnbkwV2yENGmKr6dECGbMmMHWSh/W2NjINg5lguuEeJS+B3A5AZEckIw9s3ms+qxCDB4v5UJB9B8yOYXY1+7A/DQh0iy1OCaM9shxfjQvk01eax804fUdDXjw0lyPHIeQk1EQ3Y91HVqHBJMaGrML41fcB5tz7Pv0ZHMNT9g/oIDOLkCM2IbJ4UZfT4f4ORtfiv1hS7BQ8wVmazeiUj4VDp73smMuGx+PxHApOjVmrDvSiWunpnjt2IQQQgLQYDOgbQP4QiB15hmD6JcVUimXUJMVo0CCzIkukxglR45i/mJfz4gQYMGCBWxd87fffht33XUXnnrqKSxevJiti06IR2k7TmSh+8Hq/OGGoukyCyRUyuWMNjfY2SB6urnKY4ltMrEAjy4vwM8+KsUr2+tw/bQUJEXIPHIsQoZREN2fVa1nNwPiROx95fYx7cobzTU8YWO36nhDUYHvz5ckABxRzMVU/fdQOQYwSb8bh8IWee3YTNO31bPT2SVl7+5poiA6IYSQcxvOQmcyjcWK40/36iw41DLAjhdTED3kMA0bmWz0z8p6sbtPifnqWiCGMuyI72uiv/nmm1i1ahXWrFkDPp+P7du3+3paJBRo27mtKgn+oE4/VMpFSVnoZ7Oh1o4nFwJplmq2zKqL55mbbVcUJeK94iYcaBrAn76pwKurz910lpCxoiC6Hwvv4i6sXFE5Z2+wMULeaK7hbk4X8G13ODteGsc1EiHkfBw8EYrDlmLp4H8wU7eFvfNt5XvvjvTNM1Lx/JZalLVpcLh1EJNTI7x2bEIIIQFaD/0HpVy2VnWzlV4mJKuQGE5ZVaFoXl4SG0Tf4xwPVHwJLPiVr6dEQpCL+UN0kquuugr19fU4ePAgZs+ejehoz5RpIOTMQXTfN4/sswrQbxOCDxey5BREP5uDnU5oHGKEC8xItDajQ5LlsZvOT141AVe+tAsbjnXh2/IuLB2f4JFjEcKgxqL+Sl0Hmb4ZVocLGonvTxa+cGhQjl6rCGFCB+ZGG3w9HRJAKuXT0SeMh8xlxDS9dzNkopUSXDGJa3jDZKMTQgghZ9W8i9tmzD/l6c0VPex2SQFdCIaquTlccPKoKwuaoxt9PR1CjktISMCKFSsogE68w6IHLExvNJ5fNBWtGcpCT5NbIRFQKZdzJUQeNHCfYTLMnu3PV5ikwr0LuCD9418cg8Zk8+jxSGijILq/ql7HbrY3OeDkh2bH841DWeiXxmohplpj5AIwy8X2qJaz46n67ZA7dF49/p1zM9gtUxedWZJPCCGEnEbTDgw0ATw+kDrr+NMmqwO76rgVhEuolEvIYlYgZEVJ4QQf+7qcQH+jr6dECCG+y0JXxABCrha5rzALM6p03OqwccrAKpPrCyXGoSC6pcrjx2KaimbGKNCjs+DJr8s9fjwSuqici5/XQ/+y2oYfn5qcFBKYE9TGHi6IviyeSrmQC1cnnYguURoSbC2YqduMr+G9sipFKRGYkhaB0pZB/Gd/C35GncIJIYQAaGlpgVqtZseRbZuRCcAQnovqirrjr9nfbobZ5kSsXABTZy0OdZ3eFKaxkQKqwYj5dz106NDxr3Mj+WjoB1vSpXDLq+jJuXlE+4mJiUFaWpoHZ0oIIV5uKuoHpVw6zCJo7QKIeU5kKSiIfj4HjVwiQLytDXKHFkYB1+/OE6QiAZ65vgg3/rMYnx1qx8V5sVg52fc/MyT4UBDdH+l7gNZ97PCrajt+jNBTrpOizSSGlO/ExTHezSImQYLHwy7VClzf9yqKDHsQJ1ro1cPfMScDpS2H8f6+Zty3MJttOkoIISS0A+gFBeNgNJrYr9+8SoofTxHj1fVHsOahE42woi//OZRFS1C/43NM//3r59ynzWb3+LyJ5xkt3L/j448/zj6GyfPmIvaa37JB9Ms3/Qqzb3pmRPuTy2WorKyiQDohJPD5UT30Kt2JhqIiurQ7r0GHFN2iFDaIzpR0qVCcWHXnCdMzovDTS3Lxwne1eOzzY5iaFonUKLlHj0lCDwXR/VENU/fQBUN4Htq0JQhFww1FF8boIKNaY2SUWqV5aJbkId1Sg1tiqvG2F4+9fGIi/ryuEt1aCzaVd2NFke9r+BFCCPEdJgOdCaC//9sbUZAagwk9nwFOI1ZesRyXXJfEvsbhAn7csgBaJ/DsFQkouuEnZ9zXm+tL8OpX++BwUBA9GFiGboY8csNs3HjpiRsqOocId7a4UONKRWZyFI68fANsAsU591XZ0ovVT33M/rxREJ0QEtCcDkDf5RdBdKa0Vo2BC6KPC+NuhpPza5COZ4PoOeZjHg+iMx68JAe7antxqGUQ9/z7ID67fy5kYoHHj0tCBwXR/biUiyZhHlNJCqFcD51KuZCx2q1agfTeGlwS3obxsd5LGRAL+bhlZipe2FrHNhilIDohhBBGQVospqbIgC4jwBMgt3AyIBCx3zswIIe2SQKV0I5bJ0oh4p85aJC0v8bLsybekBIbhql5p/6bFw6YUa6Todg5HivDNEBKns/mRwghXl+h77QDQikgi/TpVAbEcbA6+VAKHUiRUuPKkaqTFWGO7lukm6shdFpg53u2rr1QwMdLt0zFlS/uQmWnFr/+9AheuHkyeLzTS+MRMhq0CMXfWA1AwzZ2OJg4PzT/0OolqDVIIeI5sSiW6cRNyOh1i9NQKy0Cnwf8+RLvNqO5dXY6hHwe9jf1o6KDfpYJIYQMYRqKMsKTjwfQGVt6uHqhi2J1tFScsOZF69ktU9IFvdW+ng4hhHiPtu1EFrqPg6BqSfLxhqIUjx05tTARGkEUhLAhw+Kdc1hShAyvrp7GXod/XdaB57+r9cpxSWigj+f+pn4rYDcDEekwhzHtpkLPt0MXkHOj9QgXOX09HRIE9qiWs0vkrx4nQr5g6MOYF8SrpFg2getK/l7xUMCEEEIIGRw6J0RknPL05qGm6kvi6MYr4cyN4noD7WaC6ExAycIF1QkhJOhphq7bwlN9Og2+TAWNKJYdF1AplwvD47HZ6Ixs01GvHXZmZhT+uHICO/7Hllp8tL/Fa8cmwY2C6H5aygXjVvj8bquv66EvowtI4ib9onh8p+E+fN0m2Qa4vFdn/465XIDki8PtGDRavXZcQgghfsrlBAaauXFkxikr8RqMEnYlHjVVJ8NmRBoh5LnQ5opDizMOUFf5ekqEEOJ5zPWappUbh6f4dCqKggVw8fiIk9gQJXb4dC6BqF7KBbOzzBXgu7z33++WWWn42SU57Ph3nx9ls9IJGSsKovsTpjkU21QUQP5yhKJWkwhHtHLw4MJiCqITN/qoNw8WuwtFwmakDe7z2nGnp0eiMFEFs82J/x4Y+iBICCEkZMlt/YDDAggkQBi3WomxeWgl3uwoA8KEtBKPcBRCJ6aEG09ko3eX+3pKhBDieaYBwMb1Djn5XOkLigmXsNsCJWWhj0aHOBNGvhJSlxHJlnqvHvvhJXm4eUYqnC7g5/8pxfYm7nxKyGhREN2ftO4FTP1c04y0OQhF33RGHL+AjJXYfT0dEkR67XK8fIDLBJ/f/DKXCegFTBOTO+ams+N/722GgzmDE0IICVlh1k5uEJkO8E58FN84VMrlsjhqqk5OxZQ4PF4XXdcJGPt8PSVCCPFOPfSwRIAv9Nk0BgSRkCTmgedyIk9p9tk8AhmTxV8vHc+Os83eK+kyfC3+1DUTjwfSX9yvgbLoMq/OgQQXCqL7YymXvGWAwHcnCl/6posLol+ZMOjrqZAgtHaXFUaXGPGGKuT2bfXacVdOTkaEXIS2ARO2VvV47biEEEL8j8rSeVo99HaTCGUabiXe0nhaiUdONS+aK+9TjCKuIh1loxNCgt3xUi6+rYdePVSKJNzWC7mQkqFGq146kd3mmI95tbQqg8/nAum3z0kHc+Toyx9EvY5CoWR06CfHXzB/SKrXhXQpl25nGMp1Mgh4LiyLpyws4n5qowtfWGaz47nNr4Ln8s5qB6lIgJtmcB8A391DDUYJISRUyUWAwtp7Wj30jUP9YGZEGhBHK/HID0wON0EmcELtVKLalQp0ez8IQQghvmkq6rt66GYHD7XSAnYcZ6bGlGPRIs2DlSdGmGMQ8TbvlzhlAulPXjUeV+Yp2K8PDwixp14NF51LyQWiILq/YD4MDzQBQimQzdXcCjUlNu5icl6Unhp2EI/50joLRmEEoswtGN/9jdeOu3pWOvg8YFedGnU91DCOEEJC0fw0AfhwAhIVV75vyIahIPrllERAzkDMd2FGhIEd78YkwKI9kaVJCCHBxqrnaqIzwpN9Ng3m3Gzhy2DX9CDCNnQDnIyKgydCk4S7IZFt8m5Jl5NLu9w5KQyDuz9ivz7QNIANx7pgd1AfGhLAQfQHH3yQ/eEefuTkcN10z+X7779HQUEBYmJi8OyzzyIgVXzJbXMWAxIlQlGJnQuiX5lIpVyI55ggwf7Uu9jx7NY3IHB4p7ZdapQclxbEs+P3ipu9ckxCCCH+ZXGW8EQWOo/HDnssQhwclLPjZVTKhZzFvKG66MWCGScScAghJJiz0BWxXJKhj3zYGs1u9WXfgjtjk7Gokw2XdPFNEJ3BxBg1uz7AtCg7m+BW26PHp4faYbDQKkASoEH0kpISrFu3DgMDA+yjtLT0nK/v7e3FVVddhVWrVqG4uBgffPABtm3bhoBT8RW3LVyJUCSKSUeHMwJinpMaahGPO5JwHbTieIRZezCp639eO+6dc7kbRZ8ebIPObPPacQkhhPiHxZknBdGHfNsdDhd4mBxuQKKUzg3k3EH0fdYM2F18oLcacNDPCyEkCA0MJRxFpPtsCjV6CQ4MKtiGovqjm302j2DSJC2EA3xE27sRafNtn7AMpRPXTEmGRMhHl9aM/5a0ok9v8emcSGDwq+6Vdrsd5eXlWLBgAZTKkWVjM0HzpKQkPP744+xdpSeeeAJvvfUWFi1adMbXWywW9jFMq3Vvxk9LSwvUavUFvUeqa0KhuhpOvghHTAlwHjrEPl9ZWYlQIS+4iN0uiNEjXETLaYhnOfgSFKfdg6V1f8LMtndwLP5qWIWeXwEyNzsaOXFK1PXo8UlJG340P9PjxySEEOIfhJZBFCUKTgsMrB8q5bKcSrmQcygIMyFcaIfGLsIR6URMdZQBfXVAHLc8nhBCgsag74Pow1noadYGNOn7fTaPYMKUxmmV5CHDUoVc02HsF13m0/mkRMrZvmVfHu6AxmTDxyVtWD4xAenRXN10Qvw+E/3o0aNwOp2YPHkyZDIZli1bxgalz6WsrIwNmDMBdMbMmTNx8ODBs75+7dq1CA8PP/5ITXVft2dmrgUF4zBt2rQLevzniVXs+9dVGjFl9sXHn1+9ejX7vE7PZZ4EK6aXg2IcF0S/MoFKuRDvqIxbjj5ZBmR2DaZ0cHXRvFKHbSgb/V+7G6n+GiGEhJCwXu7zqUkYAYi5C7Q+qwD7+rkx1UMn5yLgAXOiuLroe6QLuCeppAshJNgwPR9MTNCaB0S4L1ZzIUwOHj7t4PqWFPiofnewqpZNZrf5psPwB5FyMRtIT4qQwupw4suyDhzroM9jJEAy0SsqKpCfn48XX3yRrW/+0EMP4Z577sHGjRvP+h4mk7ywsPD41yqVCh0dHWd9/aOPPoqHH374lPe7K5DOZKAbjSa8/9sbUZAWO+L3FfR+DdgHMXHmxTi4MPv48+v31+Dxf22G2eydms2+0oEoiKKSIYIdl8ZRLVDiHS6eEHtT/w8ran6HqR0f4XDSzbAIwzx+3OumpuDvm6rRNmDCpopuLJ+Y6PFjEkII8T1Vzz52q5EkQzb03OaecDjBwwSVEalyKs1Bzl/SZWNPOPbY8/FT5omBRsBqOH5ThhBCAt7gUBJlWILP6qF/3RUBnV2ANJkFyb3nTuokF6ZeNhGOwU8QY+9EtK0TfSLfXwvLRAK2tMt3lT2o6tKxW63JhjlZ0ceTdQnxyyD6rbfeyj6GvfLKK8jMzGQD3Uxw/EyEQiEkEsnxr6VSKYxG41mPwbz25Nd7AhNAn5o3wi7Sxj6gcxDg8ZExfgYyRMOXVUBlS2h0gD7q4jJzJwrboRRSZi7xntqYS9HX+iaiTY2Y3PEf7Ev7P48fUyYW4LbZ6Xhhax3e2NlAQXRCCAkFTgdUPfvZoVaSjIShp9d3caVcKAudjMTcobroJboImKNSIdW3ctnoqbN8PTVCCAmaeugftkax25tT+tFPMXS3svDlaJKOQ7a5HHmmwyj2gyA6Q8jn47LCeKhkIuxv7MeBpgFoTXYsLoxjv0fIML/+aYiLi2PLu3R2dp71NVFRUWxz0WE6nQ5isRgBg2kKNHySOCmAHiqcrhNB9OnCJl9Ph4QYF0+Aval3s2MmG11i13nluLfNyYBYwEdpyyAONlONPUIICXrthyCyajBodkEv5lYramwC7Onn+nEsi6eVeOT8suQWxEtssDr5OBi2mHuyo5SrjUgIIYGO+Vvm43rox7QyHNYoIOI5cUPygE/mEOxqZFPYbZ7Jv85fTNY5k32+uCAOfB5Q3a3Dl6UdsNop0ZP4aRB9zZo1+PDDD49/XVxcDD6ff85yKzNmzGBfN6y0tBTJySPMAvcH6qEgeuw4hKIDAwoMQgmnxchmohPik2x0WSakDh2bje4NsWESXD0liR2/saPRK8ckhBDiQ7Wb2M23dXZ29SFjU48KdhcP+UoTshUnmt4TcjbMqvJ50dwN/53OCYBAApgHgQFKRCGEBD6xQ8/VRGfOk+G+iem81RTDbpcnaBArsftkDsGuXjoedogQZe9FrO3spZh9ZXxSOFZOTmaT3toGTfjicDssdoevp0X8hF8F0SdNmoTHHnsM3333HTZt2oT77rsPt99+O+RyOVvSxWY7vVbkVVddhd27d2PLli3s959++mksXboUAcE0COi7uaYZMbkIRZ8NNewwVu+CmEd/mEjoZKPffVEWu/22ogvNfVyjMEIIIcEdRF/PBNGHfNUZwW6XUykXcgEWDJV02dkfAcSP557sLPXtpAghxA3CrF3cQJUECLxfXaDLLGTroTPuTld7/fihwsaXolFacCIb3Q+lRcnZOuliIR+dGjO+KO2AxUbxKuJnQfTVq1fjpptuwnXXXYdVq1Zh2bJleOmll9jvFRUVYd26dae9h2lA+txzz2H58uWIj49HdXU1G4gPqCz0iDRAJEeoMTt4WN/N1QLVH9vq6+mQEOaLbPS8+DBcnBfLrmB7ezdlkBFCSNDSdQOdh9nhhlouiN5jEWJ3H1fKZWXioE+nRwKvuSijXCeDOmYm96S6FrB4JwmAEEI8JczS5dNSLu+2xLArxGZF6jEx3OSTOYSKGtlkdpvvZyVdTpYQLsV1U5IhFfLRpTXjs9J2mCmQHvL8KojOWLt2LQYHB9HX14fnn38eCgXXbb6pqQlXX331Gd/DZKwzwfMPPvgAR44cYYPpAaG3itvG5iMUbe5RsV2vI6CHpbXc19MhIcxX2ej/N5SN/nFJKwaNVq8ckxBCiJfVbGQ3hohx6DVyF4pfd0bACR6mhBuQoaC//2TkmPIChWFccGe3KQMIT2E+yQCdZb6eGiGEuCcT3QdBdIOdjw+GGorenUFZ6J7WIC2ElSdGuKMf8Tb/7d4ap5Li2qkpkIkE6NFZ8HlpO9VID3F+F0QfrczMTFx++eVQKrmsnoAo5aLrHCrlkodQLuUymdfAffgnJMSy0eflRGNcQhiMVgfeKx5qokMIISS4VHErKQcT5h1/6suhUi5XUxY6GYWLhuqi72BWMyRN5Z5kguguurAnhASmghg+RE4zwBdy5Vy87NOOSGjtQmTILbg0lpp9e5qdL0GDlCtJlm/iVuv5K6af2bVTk48H0r850gG7k863oSpogugBp7fyRCkXcYAE/t2IWca8oy+MHU/h1ft6OoT4JBud6QD+wKIcdvyv3Y0wWKh5DSGEBBWLHmjYzg41Q0H0DpscR7RyCHgurEigIDq5cAtihuqi94XBFZ3HlYW06oC+Ol9PjRBCRuWSTAE3UKVwgXQvcriAfzVzDUV/nK4Gn+fVw4esGtkUdpvHBNH9/CZwjFKCqyYnQSTgoXXAhM0V3f5ahYZ4GAXRfaWngtvGFSIUMc20HC5uGXMMj2o4ktDNRl8xMRGZMQoMGm34YB9loxNCSFCp/w5wWICoLJjDMtinduiTjmcTx0iotia5cNMiDJDyneixiFBjVAAJRdw3OvyzQRshhJzPJZlDgfNI75dy+a5XhSajBOFCO65L6vf68UNVk3QcLDwpwhyDSLL6f4+wBJWUvXZnbrLUdOtRqaVwaiiif3VfMKgBQy/A44d8KZdrkwZ8PRVCzpKN/iHEdi7Ty5MEfB7uvzibHb+xs5GalRBCSBCWcsG4Fczyo1OC6FTKhYyWVODCrKgT2ehI5Bq0YaAREjuVISCEBBiXEwszhD6rh/5mE5eFfmtqP+RCSi/2FgdPhHrphBPZ6AEgPVqBRePi2HGlRgh5wQJfT4l4GQXRfZmFHpkJiGQINVU6KSp0Moh4TlyRoPH1dAg5Sza6HpM7/+uVY149JRnJETL06ixsk1FCCCFBwGE73lQU+SvYjTgxD112BWQCJ5bEUbCTjN5F0VwQfYc6DJBFAFHcDfk4w9B1BiGEBAj5YA2iZDw2qIqwBK8e++CgHPsHlGxs4vY0aijqbdXyEyVdeH5e0mXYhKRwTE3jettEL3sQzRqbr6dEvMi7xaYI2MJJw/XQQ7SUy+cd3B+cS2J1iBRT1i3xv2z0fak/xvKaxzCt40OUJt4Mm1Dh0WOKhXzce3EWnviyHP/8vgGrZqZBJKB7nIQQEtCadgJmDSCPAVJnAn1lUBQuZL81XdqF6obR3zTtUFMAPhi19mpxqKZ9RK+NsTKJKEnY2y/H3qpORPKykY96RBnqEaeggr6EkMCh6tnHbrWSREQyq/W96MV6Lqv4uqQBJEipP5W3tUjyYObJoXDqkGytR5skF4FgXk4M2nr60QMpnt49gEvn2KCSinw9LeIFFET3Nn03YBrgmmXEBMYfCHeyOU+UcrmGSrkQP1UTsxizW15HlLkFk7s+wYGUOz1+zBunp+KF7+rQPmjC56Xt7NeEEEICWPnn3LbgSoAvQFtHJxQFF7FP/e+d1/F+Q8mYD2Gy0gV/MDDquf5AT3+yj32MVPIDhUBYNBb9dQfMzWXY8yM55qQK8bOZYnR2dnpwxoQQ4j6qnv3sVitJAhcp8I4jGhm2q1Vso+/7s3q9eGQyzMkTolY2ERON+5BvLA2YIDqfx8PMaDu+qBhEJ+Lw6GdH8dKqKeANle4jwYuC6L4q5RKdAwjECDVM045eqwgxYhsujaUsKuK/2ej7U3+MZbW/x9T2D3A48UbYBHKPHlMqEuCeBZl4an0VXt1ej+umprD10gkhhARoKZfKr7nxhGvZTUmbHgJFJAQOM+67eQX4vOWj3v22HXtQunMLLHZa0RcMrBYzu529bCXmTps04veVOJxodgEXrfopJgpaUcWrwxx8g5/MEGNTf5cHZ0wIIW5i6INigFupr5Uke/XQLzVwWegrEweRLrd69djkhBrZFDaInmM+gq2u69hr8UAgEQC9X/4FKXc8i3VHOrG4IA7XTEnx9bSIh1EQ3WelXAoQij5qi2K3NyQPQETVKogfq4q9DLNb30CEuQ1FXZ/iYPJtHj/mrbPS8cr2ejSqDWw2+vXT6CRMCCEBqeF7buWhIhZIn8c+dbCfS55IcPYiLSNtTLtXRgx9niRBRRUVg5T0kTfVM+jEaO4B+kUxSEnlQ+NKRWfrTiTKNMjR7AbwY4/OlxBCxqx+K3hw4XCXA85EzyYtnaxSJ8WmnnD22A9k9njtuOR0rZIcGPlKyJ16pFlq0Swdh0Bh7azBjeOV+OiYHk98UY4ZGVFIifTezzHxPgpjepO2DbDoAIHkePOfUNJmEnHNjwDcnNLv6+kQck4unhD7Uu5ix9Pa34fQwWWJeZJCIsT9F3N/G57bXEMZhoQQEuilXApXsqVc1HoLKrVcrcwEW4dv50aCRqrcwm7VVhEMdj5cPD42mCayz40b/I5bEUEIIf6sdhO72VBn90kW+vIEDXKU3N9S4htM5nmNjFuFlW8qRaC5dpySbTSqs9jxy4/L4HC6fD0l4kEURPemnqGsIaYWOlMTPcR83BYFF3iYF6Wj5VIkIFTFLodGkgSFrR8Tuz/zyjHvmJuBeJWErY3+/t4WrxyTEEKIG9mtQNVQKZfxXCmXzw61wQkeLB3VULoMvp0fCRpygQtxEi5Q3mzkVjrsseSgS++Ewj4IHPvUxzMkhJBzcDqA+u/Y4fpa7wXR6/QSrO8KZ8c/zaIsdH8p6cLIMR2BwBVY/V6YEqzP3TQZCrEA+xr78cbOBl9PiXhQ6EVyfXmCOF7KpRDBoLajH7E17SN6rcPFw/uteex4lqAOh2pO1Gls7Bq44P2dTYea6qz7C51WO+amVlod12jLV5x8Ifan3Ikl9U9hRtt7OBJ/LRwC6Smvqax0z5L6mJgYpKWlsbXRf7E4j21O8vK2Otw4PQVh1OmbEEICK6vOrAGUCUDabLhcLvz3QCv7Lf2RTcBF4309QxJEMuQW9FhEaDJKUKgywwYhnt9nxdpLpcDu54GJNwJ8ypsihPihjlLA2Ae7UIHiVu9dx7/SGMcm9y2J06AgzPOrjcn5tYszoRNEIMwxiAxzJepl3KqqQJEercATVxbikU+P4u+bqrEgNxaFSSpfT4t4AAXRvaW/AbCZAJECiMxAIFNruAyqn72yGQDzOD9Z9kzEXb8MDqMGP3/kScBx+t3FC9nf+ZisgXX3MpiYbVwJkpKSEpRWje0urLWnkd3a7b7796yIuwKzWt+CytqNCT1foSzxRvZ5bT/XwX316tVuOY5MLkdVZSUbSL9hWgre2NGABrUBb+5sxENLuBtQhBBCAkDZR9y2iAleClDS1I/6XgPEfBcMlTsoiE7cKkNuxf4BoNkkxvAK8lcPWPHHJREQ9VQAlV8C46/x9TQJIeR0tdy1vy5uOhyuoRVcXshC/6Ijgh3/jLLQ/QePj2rZZEzXb2dLugRaEJ1x4/RUfFfZg00V3Xj0syP47IF5bJY6CS4URPeW7mPcNr6Q/QMRyHQmrhTL4qtvQNH4kTVI3e3IRZcLGKcw4sbf/PaU723bsQelO7dg9rKVmDuNq4U1WsP7olrSvmMd+m8/KTsOc2dwy7JGa+tWAw7WAg5mJYePOPkiHEi5E5c2/BUz2t7Fsfir4eCLYdJz2RIr7v0d8oumjekY3S31+OCva6BWq9kgulDAxy8vy8dPPjyEN3c24LY56YhRStz0/4gQQojHGPqAmm+58aRV7Oa94mZ2WxRuRa3V5MvZkSAUL7FBynfC7OSj08ytXNNYgMqIS1DUvx7Y9hRQcBV7Q4cQQvyxHrombhYA7wTR/14Xz5ZXY7LQi8LpnOxvJV2YIHqWuRxCpwV2fmBd//J4PPz56gkoru9DWZsGH+xrxu1zAjuBlpyOgujewGSg99Vx4/jAu6N2NlExsUhJTz/v63R2Prqbubu9c1JEiBSf+h5lBFcOQxUVM6L9ncvwvojvKaQiJEaPbQmTXMrV9/S18virMLPtbYRZe1DY8w2OJnA1bhnRSelIyXV/VuHlExIwMTkcR9s1bFmX319JmYuEEOL3yj8DnDYgcRKbONGjNWPDUa602axoK6hCNXE3JsktXW5BtV6GRqMEyqHnKyMuRZGpGFDXAEf+C0y+xcczJYSQk+h7uXIuzArfuJleOeQRjQwbuiPAgwu/yjlRXpb4h25RKgYEMYh0qJFtLke1fCoCTZxKil8vy8fjX5bjmY3VWDY+gX2OBI/ATokOFL1VgMsBKOIAJdcFOpRUaGVszbFkqRWRYsoQJ4GHyTwvSb6dHTPBdD4TIPEwPp/HnoAZ7+9tRkOv3uPHJIQQMkaHPzwlC/2j/a2wO12Ylh6JJBl9BiKekSnnVok2DTUXZdgFMmDeL7gvtq/lGt4SQoi/YBuKuoCEItil0V455DO1Cez2mqRB5IdZvHJMcgF4PFTLuZXsTEmXQHXLrHRMSo2AzmLHH7+p8PV0iJtRJrpXS7lMQKhxuICjWhk7nqCi5VIkcB2NX8kG0FWWLhT0rscBeL6+2fycGFycF4vva3rx+6/K8d6PZrLLxAgJFg8++CBefPHF419nZ2ejrm5o5RYhgaa7Aug4BPCFwITrYXM48eF+rpTL7XPSoS/n+nwQ4m5pcgubWdlnFcHMO2n5+8x7gL2vAIMtQOl7wIy7fTlNQgg5rR46ci/zyuGK+xXY2RcGEc+Jh7IpC92fS7rM1m1mm4tKnEZY+HL4u8rK06sh3DZOiKNtwDdHOjElohhTEs5fmiYmJoYt7Ur8GwXRPc3YD2jbmdtqXD30EMM07jA4BJALHMhRUudrErgcAikOJN+OhU3PYVbrv/AB7vT4MZmA+R+uGo+lz+3Azlo1vi3vwrIJiR4/LiHewjQgXrduHebOnct+LRBQzV4SwEr+xW3zlwPKWGwo60C31oIYpRjLJiTgf+W+niAJVjKBCwlSGzrNYvQLTsroFMuBi34FbFgDfP8MMPlWQMQltxBCiM847EOZ6EwQfQmg9uzhXC7g6RouC/3mlH6kyj2/qpiMTp8oEb3CRMTaO5FjOopyBVMv3z9p+3vZ7erVq8/4/chL7oZqxtV44stydP7rJ3CdZ0WYTC5HVWUlBdL9HAXRvZWFHpUJiIerFIaOwxruzuFElQlCSqAlAY6phT6j/V2EWzowl38M73nhmJkxCtyzIAsvbavDn76pxIK8WMjF9KebBD673Y7y8nIsWLAASmXonR9JkLEauLrTjOk/gsvlwus76tkvb5udAYmQbhARz8qQW08PojOm3QHseRHQtAD73wDmPeirKRJCCKdlD2AaAGRRQPJ0QH3Eo4fb0qtCqUbBNmH+WXaPR49Fxo4p6RKr7US+6ZBfB9FNei27XXHv75BfNO2079ucwOZOFxCZiGV//BgF4c6z7qu7pR4f/HUN1Go1BdH9HNVE9/QtzxAu5dJlFqLLIgYfLjaITkigswukOJjE3Wm+SrgLAi/dGPrJohwkR8jQPmhim4wSEgyOHj0Kp9OJyZMnQyaTYdmyZWhpaTnjay0WC7Ra7SkPQvzKsU8BixaIygIyL0ZxQx+OtWshFfFx25yxNU0nZCQy5Fx93wFBFCA46Wa7UAIsfIQb73qWWyVLCCG+VPkNtx23/NS/Vx5gd56ohX5XuhpxErtHj0fcU9KFkWqphcyhg7+LTkpHSu740x6Z+eOxsIBbRV6jEyEiNf+Mr2Me8WnZvv6/QUaIguiepGnlLqgEEiA6F6GahZ6nNEMhPPtdN0ICyZHE62AShiOR34+bJngnI1wmFuCJK7lyUK/vaKAmoyQoVFRUID8/H//+979x5MgRCIVC3HPPPWd87dq1axEeHn78kZqa6vX5EnLOpIkDb3HjaXcxnaHxxo4G9ssbpqUiSnGi2SMhnhIrtkMhcMDJE0Ca+oPknaKbgbhCLvNz21O+miIhhHDnzKrhIPqVHj/cx+1RqNFLESGy475MrvwG8W8aYQw6RWlsMmauqQyBLDdOicRwKdtkfk+Dh+sWEa+gILondR/ltrH5gECEUGKw81Grl7LjyRFGX0+HELexCeQ4mHQrO37sIgl4Lu/cILqsMB4L82Nhc7jYumpMqQBCAtmtt97K1kSfM2cOcnNz8corr2Dz5s1nzDJ/9NFHodFojj9aW1t9MmdCzqh1H9B5mEuamHwrKju12FbdC6YP9I/nZ/p6diREMD9vTEkXhixr+qnfZDI9L/8rNy55C+gaWilLCCHexjTgZnrGMaVusxZ69FAmlwjP1sWz419kdyNc5PDo8Yh7S7owxpkOIZAxPc6YcqyMyk4durXUJzDQURDdU+wWoKeKG8dPRKg5opXBCR4SpVbE05IpEmTKEm+AwSVFQawAM+DZGn6nNBm9cjzEQj521anxcQkFEUlwiYuLY8u7dHZ2nvY9iUQClUp1yoMQv8HUm2ZMuglQROPFrbXsl8snJiIjRuHbuZGQLOlyWhCdkbkAKFwJMDf/NzzCZYMSQoi3VX59oqGoiEu685SN1vFQW0XIkltwa2qfR49F3KtGNhku8JBsbUQUP7BXYSeopBiXEMaOd9T2UjJcgKMguqf0VABOGyCPBsJTEErsLuCoVsaOJ4dTFjoJPlahEhvsXJOTla7N3AWpFzDBmF8uyWPHf/6mEh2D1GuABK41a9bgww8/PP51cXEx+Hw+lWohgaWvHqhax43n/BTVXTqsP9rFfvngJaFXyo/4Vqrcyq6QE0WnQG05w2XeZX8GhFKgeRdQ/rkvpkgICWVM8HA4iF7g2VIuAlUstli5cpi/ze+AiCJfAcUgiEC7OIsdzxRzJfIC2dzsaAj5PHQMmlFHpVkDGv0p8RRmWS8jYRK3vjKEMGVcTA4BlAIHshVcRgwhwWaTYyY0ZhdS0I2cvu1eO+7dF2VhSloEdBY7Hvn0CJxOupNNAtOkSZPw2GOP4bvvvsOmTZtw33334fbbb4dczvXTICQgFL/MRAWAvGVs+b4XjmehJyB/KOuIEG+R8F2IcA6y4wrtGUpJRqQB8x/ixpseB6yU7EII8aLeaqCvDhCIgZwlHj1U5MV3wA4B5kbpcGms/zenJGcv6TJbEvhB9DCpCFPTI9nxrlo17E7qGRioKIjuATJbH6DvBngCIOEHjX1C4ObywUEuAFIUboQgtO4fkBBihBTP7+Nqj85ufdNr2egCPg/PXD8JEiEfO2vVeGtXo1eOS4i7rV69GjfddBOuu+46rFq1CsuWLcNLL73k62kRMnK6LuDwB9x4zk9R0aHF+qNcOaIHL6UsdOIbMXaucV6F5iz9mOY+CISnAto2YNdz3p0cCQoPPvggW2Zw+JGTk+PrKZFAMZyFnrUIkHquNF+LKwaKwoXgwYXf5XeGWk5j0KiVToITfGQK1ciJCvzQ5fT0SCgkAmjNdhxt0/h6OmSUAv8n0Q/FGOuGBnmAKLQy6hqNYvRZRRDznChSUakJEtz+sdcCEySINdYiq3+n146bE6fEE1dyyxOf/raKTsIkYK1duxaDg4Po6+vD888/D4WC6keTAMIEIO1mIHUWkDEff9lYxSYTXFGUiHEJVLef+Ea0gwuit5qE6DlTAzOxnCvrMvwz3FPp5RmSQMc0BV+3bh0GBgbYR2lpqa+nRAJF1XAplys8dghmke565wx2PFdUj/EqauQYqEwCJVokXCnTm8YLEehEAj5mZ0az4wNNA7DYqdFtIKIgupvJRUCUaSgzNHESQglz4VgywAVAJoabIBFQmQkS3AbMwBbMOykb3Xs/87fMTMOy8QmwOVz4yYeHoDHavHZsQggJedoOoORtbrzwUeysU2NHTS9EAh7WLM339exICJO4rLB0VLHjTRXdZ34R02CUKUHE9G/68qeAky7kycjY7XaUl5djwYIFiIiIYB9hYVS6iozAQDPQWQbw+ED+co8d5n8dkWhFLJwWI1aKh0rskoBVLeNKuqyaIAqKhtiFiSpEykUw2Rw41MKVXyOBJfBv5/iZG8eLIHDZAGkEEJGOUNJuFqHTIoaA58IUaihKQsRG3sVYyitGvKEKmQO70Rg13yvHZZbP/uW6iTjWoUFLvxG/+G8p3rpjBvh8Wq9ICCEet/NZwGEB0ubAkXEx1r60m3361lnpSI+mFRXEt4w1eyFJGscG0VfPPsP1CFPbYMWzQPMeoL0E2PdPYM4DvpgqCTBHjx6F0+nE5MmT0d7ejosvvhivv/460tLSTnutxWJhH8O0Wq2XZ0v8ynAT7vR5gCLmjC+pbOFW0oyWwSHEn9u4G9mDuz9C+ErxmPZHfK9ONhGXDPwX4+OAXY0HcOgQtxp7tCorfbv6irlWn5MdzTahL20ZQFFyOBQSCssGEvrXcrP/myo6kYUeYsW3hrPQC8NMUAipUQIJDXqeAkcSr8f09n9jVuubaIyc57Xf/Qi5GK+tnobrXt2DbdW9+Md3tXh4CbfkjRBCiIeo64CDJ7LQPzrQiopOLcIkQvzsEqoNTHzPWFuMyIV3orheDa3ZBpX0DPXRw5OBJX8EvvkFsPVPQP7lQFSmL6ZLAkhFRQXy8/Px4osvIiYmBg899BDuuecebNy48Ywl25588kmfzJP4cT30caeXcuns5PqJrH7q4zEdIvLSe6CaLoFV3QLdwa/Qc/HlY9of8T0rX4ZDxkTMUrRDvf2fmPbEC27Zr16vh6/kxCoRr5KgW2vBgaZ+LMyP89lcyIWjILobSbWNmJoqhAs88BImIpT0WIRoNknY5h3TIigLnYSWg0m3YlLnx0jUlyN9cC+aI+d47dgTksPx/66ZiF99UoYXvqtFZowc10xJ8drxCSEk5Gx+HHDagZwl6IubjWf+/T379C8vy0O0UuLr2RECe387YiUO9FoE2FbVg5WTk8/8wql3AMc+BZp2Al//HLj9y5BLAiIX5tZbb2Ufw1555RVkZmayWXnpKlEAAQAASURBVOYq1am9IB599FE8/PDDx79mXpOamurV+RI/oWkDWorPWg+d6Y/DWHHjauTnZo3uEC4ZtjgmsOMUQxU6nQ5ojCdWQpDAtW0ggQ2i3zkrBv3XvjCm81Tl/u+x4d3nYTb7rlY+s6J8XnYMPittx9F2DaakRfpsLuTCURDdjWKav2G3GkkKIsRKhGIWep7SjHAR1VUk/kun1R7PdhiL4Q97A4ODqO+zojhsGRZovsTU+lewNyn9gk7uarV6TMvLsnjA1fkKfFFtwJpPysA3a7FyztiWug1raWk5Pj93YLKWzrTklxBCAkLDdqB6PcATAEv/H/66sQoak42tcXnGshmE+EihyobvewXYVN599iA6nw9c+Tzw6jyg8Xvg0HvAtDu8PVUSwOLi4tjyLsxn6x8G0SUSCfsghL1ZBxdXyiX87Mk+0XFxSEm/8HMpUyq7uCMScPCQozBDLrWPccLEnxzQx0FvdSFZrMO0RB66w8aPel/dLfXwB6lRcqRFydmyrHsb+jCeKg8FDAqiu5FFmYKGASecWbmIQOgYsApQa+A+IE2nLHTip8w27uZOSUkJSqsaxrw/aw/XQHjb1q3Ysf8wvhSbUTaPjwxLFar+txY7Bs5c6+9MbP3t7Hb16tVjmBEPMSsfgWLcfPzsk3LIRQIsmZ4/5gD6uIICmIzu+72WyeWoqqykQDohJPDYrcCG33DjGXdj52AUPi7Zz375p6vHQyjg+3Z+hJwWRJdie3UP+xlIKhKc+YXR2cAlvwM2PQZ8+1sg8yIganSZoCT4rVmzBlOmTMEtt9zCfl1cXAw+n08Z5uTcjnzCbSfe4JHdV+ul6DCLIeS5cFG0DgfHfqlH/IjFJcRX1XbcMlGEfPWmMQXR/cnc7Gg2iF7VpUNKAq0CCxQURHej3sxrMP36P6Pk1SSEkv1sFjoPmXILYiR015f4J6udC6JPyo7D3Blcl++x2LrVgIO1p+6vxKbDfMc+PDe9C/+UjLwG3949NuysBBbc8iCmz1s46jk5nMC2NgM0YgUe/qoen6YmIS8+bNT7YzLQmQD6rY88g/i0bIwVc+f/g7+uYfdLQXRCSMDZ9RzQWwnIo6Gb8ys88toR9uk75qRjWnqUr2dHyCmSZA4kqKTo0pqxp16NS8bFn/3Fsx8AqjcAzbuBz+4B7toICOgykZxu0qRJeOyxxxAfHw+Hw4Gf/exnuP322yGXy309NeKveiqB7qMAXwQUrnT77s0OHnb0cVUAZkQaoBJRb7Zg9NEx21AQfTN2ZPwc4AV+4kK8SorcOCVqe/Q4NniWG93E79CnIzdzMf8TQrUE+6wCVOml7HhWpO+aMxAyUgqpCInRpy43HQ25VHza/iodyzC7qwTZriZMV/agXTKyBnMqGbev8PgUpOSO7c76xSjHJ/urgaR83Pz6Xvz7xzMxPil8TPtkAuhjnRchJPi4s9yTO0s9ubsMlcViQbi1E+O+fxrMJVtjwQP488fH0KExI14hwNJEMw4dOnTe/TQ2ciuYCPEGPg+4bHw83ituxrfHus8dROcLgGv+yZV1aTsA7HgGWPSo3/+uE+9jVk2Wl5fjuuuug0AgYL9+6qmnfD0t4s+ODDULzb0MkLv/hvPufiVMDgEiRXZMjTC4ff/EP2yqt0PvkkJp7UWythTt4dMQDOZkR6OuV48uMx+SFLreDgQURCdjsrefuevLQ7bCjHiqPUZCnF4QgXLFbEwy7MZs3SZ8OsIgujuJ+EDPJ7/Hwj98ivoBK1a9vhdv3zUT09KpYQkhxH3cXe7JXaWePFGGSsTnYdeP5OAnC/B1tQ03f/w1Yq8qgMvlRNnrj2DeE+UXtD+bjT4vEe+4rDCBDaJvqeyG3eE8d8mhiFTgimeBT38M7HgayL4ESJvF/k4VFIyD0Whyy5zkchkqK6sokB7A1q5dyz4IOS+nEzj6P2488Xq3777DLMIxLbcK4tJYLYShk8sYcqwOoNiWjyXiMjYbPViC6JFyMcYnqXCsXYuIi++AiynwT/waBdHJqBkEKtQZmCx0F2ZTFjohrAPKSzHBsBdpllokWRrQIfF+XVGnWY8/XByFf5RacbB5ALe8sRfP3zwFyyYkeH0uhJDg5M5yT+4s9eTuMlTVRw5iQf3fMDNZADtPDOHs25GasRhmF3BjZANWPcaU4BpZGa4315fg1a/2weGgIDrxjllZUYiUi9BnsGJPfR8W5MWe+w1MkKt2E3Dkv8Bn/wfct4v9nWIC6O//9kYUpJ3n/edR2dKL1U99TGXdCAkVrfsATQsgDgPyR17qciQcLmBrL1e2sjDMhGSZza37J/5nh208G0TPVX+H7Zm/gpMfHOHMWZnRqOzQQJpSiIOdFgTH7YHgFRw/dcQn2uR57DZfaUaMhKs3TUio0wkjUSGfgYnGvZil24zPJff6ZB4KMR/v/WgmfvZRKbZW9eD+Dw5izdJ83H9xNnghVHKKEOJZ/lruyV3zSujdjTUJXPN0U/5KPFe3EGaXkE0e+MsMIwS85BHvK2l/zZjnQ8iFEAn4WFGUiPf3tuDLwx3nD6Izlj8DtBQDg83ANw8BmQ+wTzMB9Kl5I/95J4QQHB0q5VJwJSCSuXXXpYNy9FlFkPGdmB+tc+u+iX866siAQRQFha0fqZr9aI6ci2CglAiRHeZEjVaAD47qcPdyF/hMTTbil/yuGv+XX36JrKwsCIVCTJ48GZWVled9z1VXXcUGhYYfixcv9spcQ5kkeRwGxXHgMVnoUVR7jJCTHQi7FE7wkWGpQoK12WfzUEiEeP22abhtdjqYlWFPb6zGfe8fhMZEmRqEEHI+EaYW3O96nx1X2tPwYNulqNFLESex4YVJLRDQ9Q0JACsnc4Hvb8u7YLaNIOlFGg5c+ybAZPgd+x9imr/2/CQJIcHHbgXKP+fGRTe4ddcaGx/7BrhmohfF6CATUAmMUMBcX9dGX8qO89WbEEzyVQ52NXmzxo6vyjp8PR0SKEH0+vp63HXXXfjLX/6C9vZ25OXl4e677z7v+0pKSnD06FEMDAywDyYQTzwr4qLbji+dihBRFjohJ9MIY1Apn86O52rX+3QuTP3TP109AU9dMxEiAQ/flndj2T92YFet+5ruEUJIsJHYdVhZ+TCUMKG4zY4/Om7HNrUKUr4Tb05pQpyESrKQwDAtLRLJETLoLXZsq+oZ2ZvSZgGL/8AOU469hCkJfnXJSAgJBPVbAdMAoIwHMi92226ZxKDvelWwu3hIkVoxTml2276J/6uOvYzd5vRth8BpQbAQ8wHNvk/Z8bOba2C1O309JXIWfvWJiMk6ZwLoN954I+Lj43H//fejtLT0nO9hgu1M8f0JEyYgIiKCfSgUCq/NORS1idIgTZ8EnsuBmZGUhU7ImewLWwIHBEi31CDVUuvr6eCWWWn4+N45SI+Wo1Njxuq39uHxL47BaKVAECGEnEzgMOPKyjWIMjWjD+G4oeEaFLsK2e/9fWIrisLd02CREG9gloRfMSmRHTMlXUZszk+B/BXgO2345AY5BE6r5yZJCAneUi4TrgP4ArfttkInRatJAgHPhUtitaAqlaGlI6wIWnE8JA4DMgb2IJjoDn6FCCkfLf1G/PdAi6+nQwKhJvoVV1xxytfV1dXIzc0953v2798Ph8OBlJQUNgv9yiuvxKuvvorIyMgzvt5isbCPYVqt1k2zDw3MDbF9yovYcby5BSoR1w2bEHJ6NvoRxRxMMezCPM03+E/sL+DrT3lT0iKx4ecX4S8bqvBecTP+vbcZO2p78fsrC7EoP45qpRNCQh4TMLyi+lGkag/CIlDgbstvIZyZyn7vjwXtWJGg8fUUCblgKycl45/fN2BrdQ9b0i1cJjr/m5jPBFe/DMuLB5GNLgwO7gFcGT7/LEMI8R8tLS1so+AfEli1mFjxFZuxWSkugunQoXPup7GxcUTH09v52NHHNROdE6lHpPjcK+Jbe7U4VNOOsehQU7zIr/D4qIlZgukd7yO/dxPqoxchWLhsFtxQoMQbpVq8sLUO101LgVzsVyFb4m9B9JNZrVb8/e9/x8MPP3zO11VVVWHSpEn429/+Bj6fz5Z/efTRR/Haa6+d8fVr167Fk08+6aFZB7//tEdhQBgDh0mLZFMdU+DM11MixG/tD7sM4437kWhrQbb5GOplE309JfZE/MeVE3BZYQJ+/b8yNPcZ8aN3SthmY4+tKEBePPfBlBBCQg2zLHh59e+QNbALVp4EP1G9jNJOMfu9JbxDuD3Nbz82E3JOBYlhyI1TorZHz9ZGv3E6d2PovGSRaJz+B2RuvRcRaAXa9gOpszw9XUJIgATQCwrGwWg8fXXWT2eK8OLlMhzucmDKpbeMeJ82m/2cZVy29YbB6uSzvUmmRBjP+lqjnms0+vQn+9iHO5ho9a5flXRhguhZAzshchhhEwRPYufiLDk2NtvQ2m/CO3ua8MDCHF9PifyA314N/P73v2fLspyvJjoTMGcew5555hlce+21Zw2iM689OTDPZKKnpo7wg2SIYxp4PFubwI13fQjh1DRfT4kQv2YUhKFUeTFm6TZjnnYdGqTj4eL5RxWt+bkx+PahBXhpWx3e3tWEHTW9uLxOjVUzU/HTRblICJf6eoqEEOI1zEXYVZW/QprmACyQ4MfKV7BrKIA+8P27WHgJk3072dfTJGRUmJVmKycn4W+bavDV4Y6RB9GZzzIR+XjoWzNeWSEDGrZz9Y0jMzw6X0KI/2My0JkA+vu/vREFabEnvuFyYZx6HWAfQHTebBx8bdx59/Xm+hK8+tU+OBxnD1TXGiRoMErBhwtLYrXgn2NRjNXC1UmfvWwl5k6bhLHYtmMPSndugcVOfeD8RY9iHAakaYg0tyCrfyeqY5ciWDA9zB5ekoeH/luG17bX49aZ6QiXj2D1GAntIPrWrVvx8ssvY+/evRCJLuwHJi4uDn19fWzJFolEctr3mefO9Dw5vxfr49FvEyLC3ofmwxuAqff6ekqE+L0S5SIU6Xcj2t6NAmMJKhQz4S/CpCI8enkBbpmZhrXrq7CxvAvv723BxyVtWDUjFfOj6cMiIST4KS3dbBPROEMN+nmRuFX8D1T2cp8/c0QafLf3E+CSG309TULG5MpJXBB9T70aPToz4sJGfrP81RIb/nT9BESb6oGKL4CpdwKyCI/OlxASGJgA+tS85BNP6DqBrgGAJ0DqhHlIFZ3/b03S/ppzft/k4GF7r4odz4g0IGaEzb1VUTFISU/HWCgjKsf0fuIBPB6qY5ZgdttbyO/9NqiC6IyrJiXjte0NqO7W4bUd9Xhk2flvRBHv8Y+UyB/Uw1q1ahUbRC8s5Jo4nctNN92EXbt2Hf+6uLiYbUpKgXL3ajCI8U5LDDuerd8BOCm4RshIWPkyHAhbzI7n6DZC4LLB36RHK/DabdPwn3tmY0ZGJNsN/N3iZty/vgdRS+6DgVYvEkKCVKK2DKvK7mQD6Ef4BVjqegmVGhEEfB6WT0xAsogaqJPgwJzrJ6dGwOkC1h3pvOD3t4TPAsISALsZKP8McPjf5xlCiB/oLOO2sfnACALo58OVcVHB5OQjWmxjg+iEMCVdGBmDxZDYg6tuPfMZ9FdL89nx27sb0aPlVlYQ/+BXQXSTycQ2F125ciWuueYa6PV69uFyudiyKzbb6R/WJk6ciIceeogNpH/xxRdsuZb777/fJ/MPZk9VJ8Lu4mFRjBYpNuoUTMiFOKycBx0/HCrHACbpT9z08zezs6Lx8b1z8OHdszAzI4ptJBw29Qp82yFia6j26U80ZSaEkEDGczkwo/Vt3Hj0XiisarwluAHXmR9Hr5kHhUSA66emIDeOekSQ4MKUdGF8UXrhjfZcPAFQeC0gkgOGHqB6PRfdIoSQYcxNtu5ybpw4tjIqw2r0UtQahsq4xGkhoN7GBEC/PAu98lwIXHbk9G1HsFlcEIepaREw25x4YWutr6dD/DWIvmnTJlRUVOCNN95AWFjY8UdzczOKioqwbt26097zyCOPsN9btmwZGzx/4IEH8Lvf/c4n8w9WTAOPLb3hEPJc+F3+hWeuEBLqHDwxilWXs+NZuk2QOvTw57qpc3Ni8N97Z+PJhVEwNZXCBR6qunR4f18Lvi7rQJeG7oYTQgIXEzS/tvxnmN/yCrpd4biB9zT+ZLgGNieQEiljS1xRXwgSjK4oSoKQz0NZmwbVXVzjvQsiVQGFVwNMf5feSq7RKCGEDOs+BjhtgDwGCB97/zS9nY9tau6GNpOBHj/CMi4kNFTHLmG3hT3fINgw1+S/Hirj8p/9rWjo9d/4Qajxq5roTAY6k3V+Jk1NTWd8nqmZ/tZbb7EP4n5GOw+PVXA1zu5MUyNHSZmohIxGhXwGJht2Is7Wjjm6b7Et4jr4+4l7YpwEPf99HHc++zlanBGo7zWgQc09mEDTjIwopEbK2NcSQshYVVZWenwfOeqtuLThL5BZB/Af16V40n4nTE4Bu3R2TlY0pqRFgH+Gv2mNXQM4VHPh2bsn61AH13JjEnhiwyS4ZFwcNlV0478HWvHElecvnXmaiDQg+1KgbjM1GiWEnMDEcTpKuXHSFLZu9Vh3t6VHBYuTjzgJlXEhp6uMXY65za8hRVuKCFMzBmVjq3/vj6vELx0Xh++qevCXDVV4/fbpvp4S8bcgOvE/z9UnoN0sRrLUiodyun09HUIClovHx/fhV+MG9csoMuxBmWIe+kUJCARREheKcpPQb7CipLmfzV5rGzChbaAd8SoJpqdHITtWQcF0QsioMKX7GKtXr3b7Pk/OPl/U8DRy+7bhqDMTTzjXoNTOBf6Yv2OXFSYgSiE+fT+Dfez28X9tZh/uYLJSJh3xnZtmpLJB9M9L2/DI5fmQCAUXvpOkqYCuC+g+So1GCSGcwWbA2AcIxED8hDHv7phWhmaTBAKeC0vjNFTGhZxGL4lHU+RcZA3swsTuL7Ez40EEm0eXj8P2ml72vL2voQ+zsqJ9PaWQR0F0cs4T11tNXDPRPxe2QyF0+npKhAS0NkkO6qQTkWM+igWar/BFzD0IJEyAiQk0zc6MxqGWAZR3aNGttWDd0U5EycWYlhGJ/PgwNqOTEEJGymzmSkT96UdLsHxm3pj2tX5/DRvsHt4nk8o2vudrLGj8BzR2AX5pvw+fOS5iy1QxZS1mZkZhWlok+Gf5u2UxcsH4BVfegOlFBWOa27Yde1C6cwssdmrOTnzn4rxY9sYRc/7eUtGDFUWJF74T5qZ53lLAqAZ0ncCx/wFTVgNCKoNESMhqP8ht48cDQsmYdjVoE2Bnn5Idz43SI0pM501yZkfjr2aD6ExJl91p98PJFyGY5MSF4eYZqfhgXwueWl+Jzx+Yd9bPrMQ7KIhOzohpKPhoeTKc4OGKhEEsih1F3URCyGl2hF+JTHMFMi2VSDdXoVnK1ToLpLIIKpkIC/Pj2ODT4dZBtrZqv9GKzRXd2N/Yj7nZ0ciNU1JmOiHkgmQmRGJqHldCbrQqW3qPj8PNbVhc9xRkg9V4zn4F3nUshRlctnl+QhjmZUcjTDqyi63w6FikpI9tmbAyYux/lwkZK6GAj+unpeDlbfX4z4GW0QXRGXwhMP5a4NC7XDC94itg4vVcvXRCSGgx9gN9Q80Pk6aNaVcOF7ChOxw2F59dDT8l3OieOZKg1Bg1D3pRDJQ2NbL6d6Iu5hIEm18szmMbgjPX3F8f6cDKyWP7rEzGhoLo5IzeaYnBUa0cKqEdT4zr8PV0CAkaGmEsDisvwjT9dlys+QL/lqxx6/61/b0eL4swTC4WYm52DKalR+JomwaHWgahMdmw4VgXDqkkmJ8Tg5RIudvmQQghI8Ek6Cx17cDCQ3/FW9al+MDxAMzgsuISw6VYkBtLjUNJSLtpehpe2V6PnbVqNKoNyIxRjG5HkjBgwvXA4feBgQagfiuQs9jd0yWE+Lv2A9w2KhtQcCvZR6u4X4keiwgSvhNL4zVjLa1OgpyLJ0R5/JWY1fY2JnZ/HpRBdKafyQOLcvDMt9V4emM1lo5PgFQ0ilJsxC0oiE5O02wU49m6eHb82/wuxFEXbELcal/YZSg0HkC0vRuTDbuwxY37Num5xnUr7v0d8ovGlglSuf97bHj3+RNlEc6Cqac6PSMKRSkRbJkX5sEsE//0UDuyYhTs0nEme50QQjxNBT3ev7sAB62J+IPjb7CC+9vDlK+YlRmNjGg5rZIhIS8tWo6FebHYVt2L9/c24/ErRtFgdFhYAjDuCq42ensJII/mmgoSQkKCwGkBeo9yX6TMHNO+NKIYVA1yN/UWx2oRRuVkyQiUx13FBtHTB/dBZe6AVpqEYPOjeZns+bp90IS3dzfh/oXZvp5SyKIgOjmtjMsvjqTC6BBgdqQeNyb3+3pKhAQdC1+G3arlWDz4CeZoNyBaeJHbjxGdlI6U3PFj2kd3S/0FvV4s5LNdxCcmh+NAUz+OtmvQoDagud+IGRmRSHCNaTqEEHJWVrsTWyq78W/nIuyPegAYKp/KZJ7PyoxCWhQFzwk52e1zMtgg+iclrfjVZfmQiceQ1RY7DshYADTtAGo3AbJIIJJr3EsICW6xhmrAaQeU8UBE2qj3w5eHo15ZxI4nqozIUVrcOEsSzDSyFLSEz0Ca5gDbB6c47V4EG+YczZyrf/lJGV7eVodrpyYjXkWrKn2BitaRU7zcEIdSjQJhQgf+PrGVXRJNCHG/o/LZ6BClQ+Ky4O74cgQThUTI1ky/ZWYaUiJlcDhd2NvQj82dIkjTJ/l6eoSQIFKhleLJI+GYtTUHD3QsxX5XAfguO5IFGtw4PQU3Tk9FerSCAuiE/ACzSoy5uaQ12/Hl4fax7zBtDhDHZLS7gIrPuRrJhJCgphQDcYbKE1noozzXMnk20ct/ARtfimiRHQuiqR8bufAGo4zx3V+B5wrORrTXTEnG5NQI6C12tsko8Q0KopPjSgdleKGBK+Py54J2JMtsvp4SIcGLx8d3kTfACT4uUnViWU7wLQyKVkpw7ZRkLBufAIVEAIOdh/ib/x9eLRmE1kx/Xwgho9NoEOPlhlgs252L5cV5eLszHQMuJeJ5A1jJ242WV36MHIkBieEyX0+VEL/F5/OwejaXNfrOnia4XGNcLsYEz/KXA2FJgN0CHPsEsJ27HBwhJLA9MEMMocsKyKKAuIJR76dMPgPy7Bls8HNZ/CCEFKUiF6g+eiFMwnCEWXuQMVCMYD1v/2nlBPZ0++XhDuxt6PP1lEIS/XkiLIOdj4eOpsHh4uGqhAGsTBr09ZQICXpqUTIOKRew45eXSyFG8AWWmezP/IQw3D47A9lKLitgc4MJS5/bgW1VPb6eHiEkADhdwFGNDC/Ux2HFnlws2jUOz9Qmokovgwh2LOfvw9vRH2D3girM5NfDoaeLCkJG4qYZaVCIBajq0rFNRseMLwQmXAdIVIBpgMtIdwZnRiAhoY5vN+GXc8QnVqLwRhda2qlW4qB8NjvOMJQjRkJ/M8iFc/DFqIhbwY4ndX2CYDUxJRy3zuJugD/x5THYHNQ3wNsoiE5Yf65ORJNRgiSpFX8q7PD1dAgJGXvDlqHXJkVWJB83SHYhWDH10idHOdD1wSNIVArQqTHjrncO4NHPjsJopebFhJBTaW18rO8Kx5pjKZi1vQBX7s3Fs3UJKNfJIIATFwmO4a/C17Ff9iBemVCDRdMmQCjlmpERQkYmXCZiA+mMN3Y2uGenYgUw4XqALwIGm4G6zcBYs9wJIX4npukrxCn4sAiUQ6WcLly7SYQHj6TBxeNDV/Yt4ixtbp8nCR1lCTfABR4yB/YgytiIYMXURo+Ui1DTrce7e5p8PZ2QQ0F0gs86IvBRWzR4cOFvE1oRLqK7v4R4i40vwT+7JrDja8XFiDbUIZhZ2srx7GWxuHt+JrsU7aP9LbjixV041q7x9dQIIT7W4wrH640xuHl/FqZuG48HytLxSXsUeq0iKAQOLI0bxF/jtuCA5H78W/QUboqoQuT0G4HESaOuw0pIqLtrXgYEfB6biV7e4aZzsTIOKLiKG3ceBlr3uWe/hBD/YBpEQu2H7LBLORHgX3hjYrODh/sPp2PAJkSMrRv9m1/zwERJqDUYrY/iVnlP7vwvglWEXIzfXD6OHf9jSy16tFQ6zZsoiB7iyrVSPFqewo4fzO7B3GiDr6dESMjZq0/AF1U2iHhOLKt9Anxn8JV1OZlEyMNjVxTigx/PQrxKgoZeA655ZTf++X09nEzdBkJISLA7nGhUG1BrDUfyvW/ieedKPFWThL0DSthdPGQpzPhxei8+mN6AQ/MP4J/8v+Am7b8QxdMBKTOAKbcB8ihf/98gJKClRsmxfGIiO/7n927KRmfE5ALZl3Ljxu1Ad3A1USckpO1+HkKbFuU9DvTJsi747czilCerknBEK0eEyI5LtesAR3Bf/xDvKE1axW4Le76BxBa8SVo3TEs93mT0yW8qfD2dkEJB9BA2aBXgvsPpsDj5WBSjxc+zu309JUJCFA/3fmOGxilHnKEWs1vfQCiYmxODjT9fgKXj42FzuLB2QxVWv7UPXRq6m05IsLLYHGy265eH2/HajgZ8VdaBDrsCwogECOHAgmgd/jCuHd9fVIWt82vw+LhOzBPVQFL6NjDYwpWIKLyaC86NIvONEHK6exdwQbBvjnSgvlfvvh0zN7uSZ3Dj6nVQWrrct29CiG9oO4C9r7LDR7+zjKoW+r+aY46vhH++qAVhTp0HJkpCUZtqKnoUuRA5LSjq+gzBimky+uerJ7ArydYd6cSGo52+nlLIEPp6AsQ3mGTPXxxNRatJgjSZBf8oagWfVkIT4jM9BhdeMV+OR+WfYkbbu2iIughdYRMR7CIVYry2ehr+e6AVT35dgT31fVj2/A48fV0RLhuf4OvpEULclHFe16tnazc29xnYzyDDlBIhwhwalP3n73j5xhzcNZ0rb3VcbxVQ9Q3gtAPyaKDwGkARc95jDgwOorNz7BcUWh1d2JPA1NjYiEOHDo349dOTJCjpsOCP/9uPn8+KOP58ZWXl2CaSfQlg1bG/y1kD2zE+lnK4CAloW54E7Cbooybi65rd+MMFvn1zj4rtx8b4TV4XLo7RY6dHJkpCEo+Hg0mrcXnt7zG18yM2M90ukCIYTUgOx/0XZ+OlbXV47ItjmJkZhWilxNfTCnoURA9R/6iLx3a1ClK+E69NbqY66IT4gWJ7ASpjL0dB7wYsq/kD3p/8PuwCGYIdj8fDzTPTMCMzCr/4z2Ecbdfgnn8fxB1z0vHo8gJIRZRtSkgg6jdY2d/nyk4tLHbn8eejFWLkxiuRHatkx2V7d2Bv/X6IeRmnrvVu2w80bOO+jsrmaiwLz31xoNZwZem2bd2KHfsPj/n/g7WHa0xlt1MDZBIYjBbuZ/Xxxx9nHyMljs9G4p3PY3ujHh/+7jbYBzpO+b5OP8oMdaZfwbgrAIseQm0bNq6WY9BIGemEBKTmPcCR/7CraNvGP8DUdbmgtx/TyrhGouBhVUof7sno9dhUSeiqjr0Mc1v+iXBLByZ0f4HDSTcjWP3s0hxsruhGdbcOT3xZjpdvnerrKQU9CqKHoE/aI/FCQzw7Xju+DYUqKp1AiL/YlrUGKZqDiDS34KKmF7At+xGECiag9un9c/HMt1V4Y2cj3i1uxr7Gfrx0yxTkxIX5enqEkBFwuVxo6TeipHkAbQOm48+HSYUoSFAhL155/iwZlxOo3Qx0lnJfJ00FchaPaMm4zmRlt/PGp6Bo4vgx/r8Btm414GAt4HBSsgEJDBYbF0R/5IbZuPHSaRf03qe6elBiisPND/0BD8WVsc+t31+Dx/+1GWbzGK4X+EJgwnUw7X8HKSoNYvb8Epg0FQijFWeEBAyHHVj3K2487Q4YI7nGhiPVYRLhR4cyYHLwcVG0Dn8saKee4MQjXDwhDqTcgcX1azG9/d84knAdnEw5wCAkEQrw9xsnYeXLu7HuaCeWH+nEiiJupQfxDAqih5hyeyJeGWoken9mD65JGvT1lAghJ7EIw7Ap9wlcV/5TTO76H1rDZ6Au5hKECrGQj9+tKGTrpf/q4zJUdelw5Yu78YerCnHj9FQ2a50Q4p/B8wa1Afsb+9Gjs7DPMb+tmTEKTEwOR1q0HPyR/P7aLUDlV0B/Pfc1U/s8eTqXzXoBwuViJEarMFZyqXjM+yDEF1JiwzA1L/mC3vOHhEFcURyHnYYkPJJgwASVCZUtbsoUFclQF7UYiupPkIUO4N/XAHeuo+bAhASKva8APeWALBK49PdAVdOI3zpgFeCuQxnosYiQpzTj5UnNEFFlJ+JBFXErMLvlDYRZezC+52scTbgWwYop6/KThdl4YWsdHv/yGGZlRSHGj8u6tLS0QK1Wu21/MTExSEtLg7dQED2EMMs0/2m6GHbwcE3iAH6dS0spCfFHLRGzUJJ0K6Z3fIDL6v6IPnkWBuQnlTkIAYvy47Dh5xfh4Y/LsKtOjUc+PYqdtWo8de1EqKTBmUlASKBi6pwzv6dqPZcFLuTz2A/0U9MiEHYBv68ymIGyDwB9D5e5WnAlEJPvwZkTQk42QWXG1YkD+KIzEk9VJ+CD6Vw5I3exCeRY/J4BVb/OgLinAvjgeuD2LwEJrTYjxK/11gBb/8yNl/xx6ObXyILoGhsftx/MRLVehlixDW9NbYRKdKLEGyGe4OBLUJJyOxY2PovZrW+iInY5HEFaG53x00tysamim01A+/X/juCtO6b7ZfJZS0sLxhUUwGQ0um2fMrkcVZWVXgukUxA9RAy4FIi9/vewQIR5UTr8dUIbLZ8ixI/tyvgp4vWVSNUewpVVv8ZHk95hLz5DSZxKivd+NBP/3NGAv2+qxjdHOlHWNogXbp6CKWmRvp4eISFPFJ2KXT1CdLdwtZPFAj6KUsIxJS0CcvGFfcScGMfHMmExoLcAIjkw4XpAleShmRNCzuaXuV1Y3xWOPf1h2K52f3C7cdCFujnPoHDfr4D2g8AHNwK3fkyBdEL8FVPO7Iv7AYeFK6025bYRv1Vv5+OuQ5k4qpUjSmTHhzMakCqzeXS6hAxjyrhM7fgQKksXJnd9goPJI//ZDcTV3M/dNJkt67K1qgdv7WrE3Rdlwd+o1Wo2gH7rI88gPi17zPvrbqnHB39dw+6XgujEbZj6Y/9yLoFQqUIKvx+vTu6AmO/y9bQIIeep5bY+/yncWnYbok2NWFL7J/brULv7xefzcP/CbHZZ2oMflaK134QbXivGw5fl4b4F2ez3CSHepbfY8cYhDRJ/9BK6zXwwv4ZFKRGYlRk1qkbAE1zVePFHCsh5FkAeDUy4AZBFeGTuhJBzYwJcd6T34Y2mWPyxKgl3utxfc8GsygRWfwq8txJo2QO8dzX3Nf3eE+J/vn8aaC8BJOHAlS+M+FrE5ODh7kMZODSoQLjQjvenNyBXyZV7I8QbHHwxilPvwdK6P2JG27s4Gn8NgllBogpPXFGIx744hr9sqMLU9EhM9dPEs/i0bKTkjr13kS9QJaog124S4eYDWeiHCraBTvxUtpWWTxESIIziaHyTvxYOngD5fVswpeMjhCrmA8D6n1+EK4oSYXe68PTGaqx+ax/aBty3FIwQcn47anqx9Lkd2FBnBI8vQJLMidWz03FxXuyoAugTuz7Dw663oJLw0OWMAibfRoE0QnzsZ1ndbNmFRqMEO10eushNnsqVcpFGcAG6d68EDH2eORYhZHTqtwLf/5Ubr/gbED6yPgtaGx8/OpSJvQNKhAkdeG96IwpVY2hOTMgoVcYtR58sEzK7BrNa30Swu3VWGlZM5K6X73//IHp09HvnbpSJHgIB9FaTBFHQ4chHj0J92xQcqlGMab8daq3b5kgIObdO1SR8n/kwLml4BguansegLBWNURchkK1fvx6VlZWjeu9sFyBKFuObDhn21PexwTymEemqmWNrOhroDU4I8TSt2Yb/900l/lvSyn4dpxDg6Fu/wXW//j0i5aNovulyYl7zK5jZ/i775XtlVogKp2OVKHjrVRISKJiEm8fyO/Hzo2nY7poIYXi85wLpTHNRJiO96wjw7hXAbV8AYR46HiFk5DRtwKf/x5ywgWl3AkU3juht3WYh7jiYiSq9DAqBA29PbcSkcJPHp0vImbh4AuzI/DmuqfgFpnT+B0k85mc6eDHXw3+9vgg13TrU9uhx//uH8OH/zYJEeOGJLuTMKIgepNpMIqwaCqCnyyxYov4cpTo1fvbKZgDMY+xMVrtb9kMIObeyhBsQp6/GhJ6vsKL6t/hkwj/RHVaIQNPVyjUgevzxx8e8L2FkEqKX/wJIKcRvPz+KDcc6sfbaiUiJlIdkgxNCPGlrVTd++9kxdGm5bJY752ZgSbwJ858oG9X+BE4LltY+iXw193nkcyzBHV98ivcLaYEkIf7iqsRB/Kc9CsX9SkQtexBO1xHPHChhAnDXeuDdqwCm2eibi4Fb/gPEB+Yyb0KCgkUHfHgTYFQD8ROBZX8Z0dvq9BI2gN5uFiNGbMM70xrZhsWE+FJT5DzUR16E7IGduE24Cc8guCklQrx++3SsfGkXDjYP4DefHsWzN07yy0ajgYiC6EHomFaGHx3KQI9FhAy5BR/NaMBb/xtgv7f46htQNL5gTPvftmMPSndugcXucNOMCSHnxOPhu+xHEWbtRvrgPqysfIgNpA/IMxBINAPcMu1pS1ZifP7YGok0Nbdix4e/wf3P/Rff9Sqws1aNJc/uwM8uzcHd87PY5iqh1OCEEE8YNFrxx68r8FlpO/t1RrQcT18/CTMzo3Do0KFR7VNqG8RVlb9Csq4MDp4QW3J+hy/qmYaCn7p59oSQsWCutZ8qbMNlu3KAjEn43mbG5Z46WGw+F0j/4HqgvwF46zLgureA/GWeOiIh5GwcNuCTu4DuY4AiDlj1ISCSnfdtBwbk+L/SDAzahMiUW/DetAakyqmJKPEPzMpu5jp6oqABN40P/jBoZowCL90yFXe9cwCfl7YjMVyKXy8b5+tpBYXg/+kJMVt7w/DTsjQYHQLkK014d1ojEqQnMsajYmKRkp4+pmMoI0ZXhoEQMnpOvhDf5P8F1x+7D/GGalxX/hN8PPENaKVJCDRJycmYXDT2DLMdTDmIGAt+dcsy9g77/qZ+tlb6/w624Y9XTcD83JiQaXBCiLt9W97FNibq1VnYYNrd8zPx8JJ8yMSjXw4aYWrG1RUPIdLcCrNAiW/GPY3WiBlA/fdunTshxD0yFVYs5R3EN65Z+NQyDfcY6pClsHrmYNHZwN3fAR/fDjTtBD66GbjsT8Ccn4ZcU3VCfMbpAD6/F6jbDAhl3KqQiHMnhLhcwNvN0fh/1Umwu3iYHG7Av6Y2IUpMCXfEf2hkKTiQcifmtL6Ol5ZL8aRLh2C3IC+WXan96//9f/buAzyKqusD+D+990JCQkjooYsioCKiWGj2AsiroNjbZ+FVbIgi2Du+CvaCvYsgVaQjvSS0EEhIAklI722+59xl1k2DkGyy7f97nmH77J1JyJk5c++5O/DuX0kI9nHHlKGdLN0sm8dxs3bk85QQNQO2JNCHhhTiu7OTaiXQici2Vbj64qdeb6vJUfwqMnHtrrvgX2boIerIOoX54ps7BqthaqG+7jiYVawmHZ3y6SYkZnAOB6LTcbyoHPfO34I7Pt+sEuidw3zww13nqLkHWpJAj8nbgPHbJ6sEer5HJL7p+6EhgU5EVm2Q016UHtqOSrji7m0dUVbdiglt72DgPz8BA2421GFe/CTw/S1AWX7rfScR/ZtA/+UeYNcPgLMbcN0nQNSZJ/2Is6cvXsnsjxl7olQCfWxEHuafdZAJdLJKG6Mn4XBNO4R6O+Mm7UfDFSA7d/1ZHfDwxd3U/ZkLEvHpWkN5VWo+JtHtgBzMPpnQHk8lRqEGTrg+KgcfDUhWkwIRkX0pdQvCD73eQa5nBwSUp+P6nbcjqITBUGq8XT0gGssevkDVa3Z2ApYmHsOot1bhvq+24mBWkaWbSGTVNE3D7zvSccnrf+P3HRlwcXbC3Rd0xoL7h2JATFBLVox+Gd/iqt0PwLO6EOl+ffB134+R482eMES2QOLp8QWvwc+pTE0U+HRiVOt+oYsbMPZNQw1mJxdg94/Ae0OBw+ta93uJHFllqWEUyPavDP/vrvv4lOWUtmSUIfKWd7CuJBKuThqe6p6Ot/qmwNvV/hOTZJtqnN3wfuXlqKzWMBA70SvzNziCey/soo7pxfRfd2Pu30mWbpJNYzkXG5dc7I57tndEQqGhTtnDXY7i3k6ZHPVIZMeKPcLxXZ+5uGbX3QgpTcYNO6fgl/hXkeHfD44uwMsNz1zeCxMHd8TrS/dhwY4M/LY9HX/szMDI3hGYfG4cBsQEcmIVqje5rNSwN5fQ0FCbqoefWViGp3/ejUW7j6rHPSL88PK1/dAnOqBF63WuqcQFya+i31FDvfOEsNFY2uVxVDu7m6XdRNS41KwCbNnX8tFqyUdzUV10HLd6rsJbpSPwbVow+viX4j8xhnlOWoXE6MF3AVFnAT/cAuQdBj6+DBh4G3DRU4Cn4W+To//tJjKL4mzgm/8AKWsBF3fDfATxYxt9+7GCMtWj9bftuXD1C0WUWxHeOzMDfQJK27TZRM2RokXgqRXleGGEJy48+BKO+fZAto+hp7a9kvPeqZd2R40GvLcyCbP+2IPsogo8dlkPOMuVcjotTKLbsN8yAjBtdzSKql0Q7FaF1/qk4IIw9rYkcgTF7qH4rs/7uDLhAUQUJeLaXXfjz67TsdnSDbMSXcJ9MWfCANx9QT5eW7wPy/Zkqt61svSLDsCkc2NxWa/IFpWnIPsgSZge8fFqcllz8fL2xp7ERKtPxkjv8x+3pOG5BQnIK6mEq7MT7hneRS2nMzlvQ/zKMjB67zREFu2GBies7ngvNkX9h7WNiVpZSZGhzutL321Qi7mEFCXhkR698dL+SExPbI/2nhW4KLyVa8p2GAjcuRr48wlg6+fAP/OAhJ+BC59ESsj5iO/VGyUl5kvceXt7ITFxj9X/7SYym/StwNcTgYIjgIc/MP4rIPa8Bt9aVF6FD1YdxPsrD6K0slqNUsnd8CPmX+eLPgERbd50ouZ6aU0Fxo3oj/41ezBmz2P4qt8nKHf1h70n0h8b2QNB3m6YvXAP5v59UJVAff2GfvDzdLN082wKk+g2KLvcBc/uaY9fjxqGV58dVKSGTrH+OZHjlXb5rvf7GLX3CXTOXYXR+54AXIfga+aojHq1D8CHkwZid3o+PllzCL9sT8f2I/l48JvteNJ9Fy7pFYGevmWAM5Ppjkp6MUoC/cZHX1aTy7bUsZQkfPniVLVea07EHD5ejCd+2oXVBwy9OHu191e9z3u2b/lJRKecv3Hp/hnwrCpAmas/FnV9BsnBQ83QaiI6lYryMnU7+LIrcM6ZLR+htmlHIv7+7Tvkl5TjnrgsHCrxUL3R793REV+cdRBnBprvAmSDpNf5Fe8Ava8B/ngEOH4A+O0BhPtE4+ouVRg55lr0iGnX4q9JTMnCxFnfWv3fbvrXunXrcPDgQbOtr1OnThgyZIjVtc3b2xsdO3aEuZSXl8PD3Q3hB79H+8QP1KixMp9oHDz7OZTleAM5W2q9v6C8Bn8mFeO3fcUoqjCUauke4oaLgnLw6AsfIXnI9fB0blkN9PRszmFEp1ZYUICMjIwWrSMvL09m3MBcp/F4wf1/aq6esYlT1bxj1jBSMjExsVVHV90xrDPC/T3w6A87VenTK+aswVvjzkDvqJaNPnUkTKLbEJn34Pv0IDy/NxJ5la5whoa7O2Xi/zofQws7jBGRjapy8cJv8S/j3MPvYmDaZxjtug5/TfLGT1orDrO20WT6y9f1w6Mje+CrDSn4+p9UpOWV4qetafgJQPS9X2BDtgvyfPIRE+wNf16RdziSQI/u2gv2rrK6Bh+uTsYbS/ehrLIGHq7O+L8R3TBlaBzcXFp2MOFSU4FzDv8PZ6V/oR5n+PbCH91nocCzvZlaT0RN5R8cimgzJN72p/1bLkUGkjzf8wgyytyw6rgfbt4Uh8/OSsaA1k6ki87DgbvWGXqj//0yPIuP4POrvFDhvBruHmcC4b0Ar8DWbwdZBUlSn3POuYYJaM3GCWvXrmlxIr112mY+nYOc8f4YTwzoZEgF/bynEjf/nICC8mtM3uUEj+ie8O0zAt7x58PZzUM9W3n8CPJWf4nDe1Zh8Yl3ysUncymtYKdAqq+s0nCRZtOmTdi6p2UXpyoyk9VtVqkTfun3Gq7feRs6FGzBxfufxaJuzwJOlkmsFeRkqduJEye2+sjYq86IRqdQX9zx+WbVG/2qd9fggYu64vbzO7d4JKojYBLdRiQWemLmnkisyfFTj3v5leKFXkdYe4yIoDm5YHXsfTjq2wsj9jyF82KAM7XXsDZDw46Iqyx2MGCNQn09cN9FXdUEK1tScvHrtnT8vCUV+fDDkRLgSGKmsbZ6RIAnIvw90c7fQ32upQlGIktbsSdTlW6RA2ZxTucQzLqqD2JDfVq87naFu1Xvc5mnQWyJHIdVsferSZyIyH64OQPv9z+EW7bEYX2uL27eHKcenxNi+LvSqlzdgSH3AANuQtpP0+GyaR4ifEuAQ6sMi18EEBYPhHYDPANZPsqOGXp5azh/7HWI7dihxes7dDhVjbhI37cViPMFSnKAUllygaoKoKYK0KqBmmrA1RNw9wbcfQ2Lbzjg3x7wCVMjG83Ztp3bd2DrqqU4f8L9OOvcC1q0LjetHL0PfoRrPNfDy80JNXDBEf+zEHNBV6wY7oTSGhfsLA3B1tIwbC4JQ3a1Yc410ck9H5cHJOPc2Ay4nNUXQF988Mcm/O/XDRhx5XXo2yu+RW1b8fdatZ3lVS3r0U72qeLE70W/zuE4Z+AZLVrX+rWVWLUfKK+oQLZPV/ze40VVHjU++091zLqky5Pq3LqtlRYZRmOMvuMJdO97ZquPjO3XIRB/PDAU037cgT93H8Mri/fhx61peHpMTwzrFsb5w06CSXQr5+Ifjo9Kz8XGtXGqpqiHcw0e6nIMt3bMYu9zIqrlQOiFWFZxBNccfR1DOwIXHXwB8VkL8FfcwzjmZ/89bE+HHBic2TFYLWOiyjH0yokYfveLyHPywdGCMuSXVqpl79F/6736e7oiyMcdwd7uxtsAbzf4uLvwQIOs2oHMQjz3eyJW7jP0cgnxcVejMq47M7rFv7uuWgXOO/Q2zkz7As6oQbFbMJZ1noakkJad7BOR9fJ21fDRgORaiXTp3HNNVF7bNMDDD8e6TcSQia9j78sjEeuUZph8tPCoYTm4AnD3AwKiDYskOL1DDJMmkl2RJHX/vqd/jOtZXYTIysOIqEhBcOUxeASkIqS3H3yTpwHvNbMxknjzi8TwKj+8cokHArqVwDvOD1luUahpZlLueOYxbJXOHe2imz1aTsq1xGctxJCU9+HnJ51FnHCouh2K+9yI3RXtkFDghd0FXtiW74VK7d8Eg69LNUZF5OOGqBw12sRJdcqJMr7efuM+dRscGtbiUS++geYrYUH2y8fTDZEhLSs76O9VOw6kBA7Coq7PYuS+p9Er83c4a1VY3OVpi3UCCWnfsc1Gxgb7uOO9iWeqkdmz/khUnWwmffwPBsQEqg5nw7qFw6UNJx4tr6pWczTJ/Asl5dUoqahCSWU1KqpqUF2jqaWqRlPXx2UeJ1cXZ5QXusDvzMYnQm4NTKJbqSOlbvi9ZiCibhuPDVWG/8BjIvLw365HEeNdYenmEZGVytYCccGnJfh0+n9wnfNStC/ciQk7JmFvyAhsiJmC494tr/lsb+TgoDxtD3oGViO6aweUV1arRLosxwrKcTS/TE2gVFBWpZbDx0vqfd7P0xUBnm7w83IFip3h2eksi20Pke5AZhHmrDiAX7aloUYD3FycMPncOHVg3OKSRZqGK3u44pGcpxBWfUw9lRh2mbpoV+bGkgpEjpBI/+TMZDy8qwMWHA3Ew7tisLPAG9O6Z8DDuW3KWFRUAznenRDbbShQUQxk7wMyEw2TJFYUAlmJhkUnvdN9Qg0Jdc8gQ/kXryCVlCf75qJVoGPZPnQu24no8iQEVh9vICvipDqtOcnviFcw4B1s+P2QnufOrob5cySRXFUGVJQAlcVAeaHhwk3RMUNP9YIjkAJmDw+R8iergKxVqHRywzG3GKS7xyLdPQ5pHp1Q4fxvL+/GSNyW5Luzpy8q4YKisirUaNqJRV7XVLnXanWroabG8Jw8lqRTVXkpgvJ2wj9/L1ZVa/hZG4/DWjvsKw1Apfz+1y59rsR4leOC0EJcEFaIIcFF8HKxzpI0ROa0L+wSaE7OGLX3ScRnLYJveRZ+6/Eiyt3sv064dKa5ekA0Lopvh7eW7ccX6w9jS0oebvlkE6ICvXDNgCg1h5jMnWSOTmNFFTVwj+iK1GJnpB08jrwTHdckeS7n26fPBV6dB6ItMYluZbbmeeGDw2FYeDQANRLEXYF4lwy8NLCIpVuIqEnkwHqJ01CUnHE7zjv8LuKz/kD340vVkhQ0FNsjr8PhwEFWXebFWauGZ00xvGqK4FFTBletUi0uqIKTpsHN/QiK4l0RXbQNONDO0OPM3cdwIiwnP3K/mTzcXNAxxEctQk5MJKjnFlcip6QCucUVxtvCsip1VVwCvywGrggYZFpXkqhtJWYU4J0VB/DHzgx1gi0u7tkOT4yKN0vpFhzZhG5rHsBPN3gD1cdQ5BaKZZ0fw8GQYS1fNxHZDE8XDW/3TUGsdznmHGyHT1JCsSnPGy/3PoJ4P8MEp21G4n77MwxLdSVQmA7kHzEskuCsLAHK8gyLTE5qyskFPZ19MGeUZ9u2mVqVZ3Ux4soT0Ll0J2LL98JNq90RLcc1HEfdYpDpHoUtqaWY+9mPmP76hxj/n0nqdTm+k56R5ZU1KK+SpdpwW2lyX3+9ohLlxfkoLzyOfTv/wcHEbegQGQYf12p1XF5e4Y7yIjeUww1lOI5CJz+1FDv5oASeqIIzqjQnVNc4oVqDui+5AMRejw4PXA+5FJS4xlAu7fRIGYc6pRxO/Jr7u1ahl38ZevqVopd/KQYElCDWh531yDHtDx2BX1y8MGrvE+hQsBkTdtyMhd1m4qhfbzgCKWX61JieuOP8Tpi36iC+3XREzR/21vIDapHSpgNjg9QEpJ3DfFWCPdTPHR6uLpDO6irBrkElxPXz5KyicqQcL8HhnBKkHC/GoeMl6vXIm1/HRrmOeTynXju83V1U5zQfd1d4ubuox/Id0mlNep/LrVwslB7p1dUajmcdxd+7lkshnDbbV0yiW4HcChf8fjRQTRq6Pd/b+HwXpGPN13Pw/m090Cegn0XbSES2p9gjHH92ewaboyZgcOoH6HL8L3TOXaWWfI9I7Au9GPtDLkSmb3docsWujfg4lyOsIg1+1Tnwr841LFU58KkpgFd1EbxqiuGpneKioR9w7/XeQMZc4Iu59V+XpLpfO8A3wnDrFwkExQLBcUBwJyAgBnBp2jbLQYG3u6taooJq9xySEywZclZQWomCskoUlFbhaGYWtidL955LTmu/kGWkFVTBo0MfHC93gltBGZydZHigk5pwUz9oswUy5PH3HRn4emOK6kGik+T5fRd2Qd/oFvYOl2x86gZg7dvAnt/hK99ZqWF1wBXY1+chVLqaITlPRDZH/kRO7XoMZwWW4KGdHbCrwBtj13XFlNgs3NMpE36uNW3fKBc3ILCjYdFJT/XibKAky1DnujTPcCtJdblwX12AmADb+HtPQHk14OIXppLQR8tcUVHjjIoaJ7hUlcC/4ii8K46juqoYW+CKdRiAcgxCEXxx3CUE+c6BKIY3KqtcUFXphOpioMS1EkXXj8Zze0Iw8+lFKkEuSZrm6QlE9jTcNeM8mXqiysXJSZUzkOMVeU4Wd60CHlqpOo720MrghxL4O5WoY5kS31gU+8XBw90Nxw/vw9K5M/DOLedgyoXxnDaAyMShoHPxTZ8PcEXiwwgsS8MNO6ZgQ4db8E/0zah2Nkyua+/C/T3xxOieePiS7li066jqkPP3/ixkF5Vj4a6jammpqqIcRIQEIjw4EIHebgj0ckOglEn1cjvtiU2PVKRjYcJfaEtMoltIfqULVmb74rejgfgry89Yf8zNqQZXRObh1thsbNmwHssObwfQw9LNJSIblu3TDb/3eAlBJYfQ7+j3iM/8AwHlGRiY9playl18kOHXB0f8B6ir7bleHVDkHt6s73KpKVdD4PwqjsG3/Ji69ZPb8qO4OiQRXz3mBz+P7wBDaeaTkiG1pc7eqHDyQpWT24nFFRqcUVxchKz0I+jRvSvCAn2B8iKgoggoKwCqyw3DuI8X1u9tppNhuYExKqEeXROAO89yQ5eKBLiUh6HYPazJk5FJglUCviy6IzVHsWr9dwAea/J+I8v540AxIibMxl9SkeRYar3X3V2c4enmDF9PV/h6uMLP0+3ErSv8PFzh7+UGT7e2n4BIyAWc1fuzsXzPMfyx86h6rP9eXtY7AvcO74L4yJbVjlQ9OhN/BdbNAdI2n3jSCdkdLkW/B7/DuBeuRjQT6EQOb3hYIf48dx+mJ7bHwmOBeC85HN8eCcbdnTIxPjoHPpZIptftqS5LUJ3azVqNKsmxf/9+zFr1C955xFINpNPxR4YXou/+GJsAbEozfUUuGEtBlUY0Vi3A2Q2u/t4okder679JekCqi+tuLicushsutHu4Ge67649dnZF+JAWrV65A9569EBEaBBcnTc1lpm6dNHhrJQirOorI6nREVh5BSE02PJwq4YEKeEBuK+HsDBS5BONwThm27tqP6DNHIKbHGXDSauBaUw7vylx1fB1cchCBZUfg7HTi/5cL1PHyweDzkRA+BslB59QaebotrQwVx5Lg7XQmE+hEDTju0wVf9v8CFya9gB7ZizEkdR56ZC3C37EPqP9XjvIfR85trjwjSi1lldXYmZaPTYdysf9YIZKyilTJU0ms173YKOdMIT4eCPJxQ7CPBzoEeaFjiPeJUd7eyEnZh3MHjcENc35EdNd2sEVMoreRqhpgT5EXVh/3xfIsP2zO80G19u9/wF5+pbiqfa5KoId5GE6CGyhTRkTUbLnesfir0yNY1fFedMpdg67ZSxGbtw4e1cWIzVuvFp0cgGe6+2Pyf7wRWPMxvPctRYWLj0psu2hVatIVmahIPutZlQ/PqgJ4VebBqyq/8QaoPLPh716Jsy8KXIJQ6BKEAtdgFLoEqvulzj7qNbktd/ZW9ekasi1jNz775DV88cVU3HjjjbV7y0p9Shm6rdeoLMwACjKA3ENAzkEgN9lQz1Lu5xyEXC7432gvIO8lYNNLqHD2Rq5XR+R4xahb06XKhUO97U2gpzMqsg8jKCIGTi5uhiGC1Roqqg0npHIri9TDb4ycNEsyXSafdS5zge8Zo7Alowz+mUWIDvIyW5I9p7gCu9Ly1YHsuqTj2JB8HJUy7vsEOTi9YWAHXHtmNML9WvC7WlMNHF4L7P4RSPgFKDlRO9bFA+h3AzD4HqQcKUF64bdm2CoishfhHlX4X/8ULMnMw+x9EThY7ImZe9vj7aRwjIvOQa/KNpp49HTIcYZnAAo9IrHuSHPqsZIlBNTkw7kmCH7O5fBzKoEvyuCDUng7lcPF2QVVrt4od/NHtbMnXJ01uMhEdE5ya1hcnf5NastryQcPYuE3n2H288/hmisvNybEJUkuF9NlArum+vLLPfh10dvo2uUh9A9taIJAORaORCkicRBn4lh1nqrT3qH8AAIlqV55HK7Shb36GHoGACPPlZTNX0By470t8z3aI83/DCQHn4tDgUNQ4SrjxYioOcpd/VQpl6TgYRiW/DqCylJxxZ5HcMwnXvVKl+drpEOWg5DzmIGxwWqpS6szR8OpepJvSbfecrJN5Tg/+TakRjyXumFvoSd2FHhjc643tubLle3aJ9FdfcowIrwAV0bmortfucXaS0SOpdrFE/tDL1KLk1aN0OIDiCrYiuiCrQgt3g//8nRVf7y983G07yRhYjeQtbvJ66909kCRezsUeoSj0KPdifvt8Pc/u/Ht/PnoffkUnDtkUOtsnPQO8PQ3LKFdG36PzLwkiXWVRE/CsYQ12PjHlxjcMxoh1dlwrylBu+JEtdRV4BFxIsH+b2Jd7que+1ZcY54ad11PP7zwn3swXvWI6GZ8vkZqoVbXqN4XpRXVqpe31MA33BpmjpfyPVIvX4Z9ZxWWq0W6gYVccjdmrsrFzFUr1bra+UtPDG+E+3sg2Mdd9dAI8XWHl5uLOjCVRSb9lIR4xYkaq8XlVYYJbvPLcbSgFMlZxUjPr19jODbEG8N7hOOSnhEYFBcM5+aWn8lPAw6tApJXAQeWAkUmwzV9woGBtwJn3Qr4hhmeO8JL/UTUsIvDCzA8tADfpwfj/eQwJJd44P1Dcsk6HBE3t8f8nYXQQnLQv0OQzZTMIuvyuNuX+ND7H3W/Cq447NkdSZ59cNCzF0pd9ASyXPwuatL6cmryVQ/tdp41xjlx2kqxSyD2ep+pFiHH5kFV2QitTMexPf8gP3kbeg84Gx3at4Pm5IIqJ3eUugeh2C0UOd6xOO7dGcXuoW3aZiK75+SkJhxNDjoXZx/5GP0zvlXnhmP2PqbmAkoMH4XjTu5wdE6qxJSc/ThOLLe6JPquXbswefJkHDhwAFOmTMFLL710yllgv//+ezz88MOorKzEq6++ivHjx8MSFuwrRrsbX8R/DndDyaF/h/br/FyrcVZgsRruODysAB289EnoiIgsQw7Gs3y7q2Vb+3HqOSetCn7lmchc9x12/DYXl1x7Ezp1jIZ7dQlkmqNqJ1fUnFikd3qpWwDKXP1R5hqgDuLltqGhblsqirD3eA26aRYOPTJGNiDKsMQNRZpTX1x+0wd4aM6LiOncTQ2LDSo9bFyCT9xKL3v/8qNq6Zi3odYqK509kesVg3zPKKSXuCG7X/0YQOaJ+W1FktFezi4q0R3073Ql9VRKL/UTM8tLb/W0jKPYsXkj4gcORXaphuKKajXkURZziAv1UZP69O8QiOHdw9Ap7DR7m1VXAfmpQGYikLkbOLYbSN9mGKFhyjMAiL8c6H0NEDu0yXMIEBEJ6Ywmvc+vi8rBsix/VdpleZYvPCK64PvEInyfuE6VQjuzYxB6tfc/sQSgfaAXE+tWzhpid6rfWVizZg3yOo2Ec7eLVQcOezo2z3Frp5ZlOblY8OcGjO05FsO7X2HpphE5HJnzZ03svdgSdSPOSP8avY/9DN/KbENZVA/g/kd8cbjmUxSnp6vRIMe94xymfrqjsqozovLycowdOxaXXnopvv76a9x///345JNPVJA+WRCXofxz5szBoEGDcPXVV2PAgAHo3r072trR4ip4RvdCiWaobd7FtxzxfqUYEFiiJtvp5isTlrV5s4iITotMMlrg2R67a+Lw9a4quF83GHlRw+AIapzdkOMdp5a6PCvzjAl10yWwLBVuNWUIL96nFun/XtWXSfTWiPnWyM3FGSG+0rPccMAcWpaGZT89j0VPbsYZZ5yB3JJKpOaUIDW3BNmF5aokS3axYdb6kopq1dNderJLD3R3Y11VZzUbfTt/T0ScWKIDPREfKXMKOBvKEVWVA1V5wPFjJ+6feE7KGalJ82TJMdyW5AAF6UBeClCYbqgBXJeMpGh/BhB7HhB3PhB7PuDKHjZE1DLSQ+2S8AK1LE/MwuXvbseVdz2OndnV6uLj8j2ZajGtPR0Z6ImoQC+VUA/xMUw2FnBi0jGZgEzmpEjOq4RrcDSOVXrhWJkr3J01eDjXqNvTnJeMbDB2p/n2wc0/l+Gme+PQnwkrImplpW5BWNvxLqzvMAWdc/5Gt+yliMleiXCfSoRjJ5C8U71PSp8WeEQiz6sD8jyjVQezErdglLgFqY5mlS5eqHL2VB2w9PvVznLe2NyJjMmhk+gLFy5Efn4+XnvtNXh7e2PWrFm45557ThqUP/jgAwwfPlxdBRf33nsvPv/8c8ycObPRwC+LTr5PFBQUtLj9Z4XW4OOXZmPykAj0DdHgnKsBcg6bAqyCYTkdGxJS1O2iTUlIya1oUds27TmibpMPHW7+UO8TjqWnq9v01FRs3NTy4dzmXJ+1rsua28bttPz6UlMNMyKl7t2JjZ4tOxE4lCCTEQMHdm1GRVmp1axLpCbtMdwePoSN7i37O5SSYthnK1asQElJSYvWdfjwYXW7a9NapB+pP7FkfZJYlFS5oVyMi1aNEOSiHbIRilx4FR3BX7vW4+aiohbHFv3zUm/O3pxOzG/N2O23eQ723esD90N3wCX138yL04mD2X9/U7UG7+vv0+9rNRoeecgXbt+NQK7MLwugA4CYWt+qr9v05/rv99V+vvY7Wr7FMq+aK/LdwpHvEYl890jkubfHcc+OqIIncEj+80uv9Do901v8/6ZxKQcS1O2KrUkoKa+0muMda45D1hzTrHVd1tw2a12XHrPF2oQ0eCwwlM9orsPH8lC8ewViM4ZgaMeOSC91QUapC46WGZaschdUaE44XFqMwxmnXl/kTa/hjiQAstQhf0f15d/HwvSx4Tm5ralpj+DRD6GIsdsmYrd+7HfgwEFUVLR8pJc5jys3bNhgtraZ8xzBmuMt/9Zbfn3Wui5zr88c/6cMs4gNQ9puD5Rt+gYTRp+DgSGl6ITD8EE5nMrTEFSQhqDTWOctTsA7j/qipuQRaMudUXMiQtXA2Rit5Lmm0mqqcc99PvD67jJk/mCOuZnku/+NaaZnRacjoqYa++/1gduhO+Cc6tLs9RjbUV2NPld4tm3s1qzIM888o40cOdL4uKamRgsKCjrpZy644ALtxRdfND5eu3atdtlllzX6/unTp8se4cKFCxcuXGxmSU1N1ezN6cR8xm4uXLhw4WJrC2M3YzcXLly4cIFdxW6r6okumf+4uLjaRepdXJCbm4ugoKAmfcbf3x/pJ65UNWTatGl46KGHjI9ramqQk5ODkJAQq6nDaq1kX3fo0AGpqalqP1PLcZ+aF/eneXF/Wn5/ypXwwsJCtG/fHvbmdGK+LcRue/r/wm2xTtwW68RtsU6W3BbGbuuI3fb0+2xJ3I/mwf1oHtyP5sH92PzYbVVJdFdXV3h41B5S4enpqYZUNZZEr/sZ/f2NkffW/Y7AwMAWt92RyH8y/kczL+5T8+L+NC/uT8vuz4CAANij04n5thS77en/C7fFOnFbrBO3xTpZalsYu60ndtvT77MlcT+aB/ejeXA/mgf34+nHbquadiU4OBhZWVm1npMrAe7u7k3+zKneT0RERLYZ84mIiMhyGLuJiMiRWVUSfeDAgVi3bp3xcXJyspqMRIJ1Uz+zdetWREVFtXpbiYiIqG1jPhEREVkOYzcRETkyq0qin3/++ao2z8cff6wey2zfI0aMUHXW8vLyUF1dXe8z11xzDb7++mvs3LlTzcj61ltv4dJLL7VA6+2fDMebPn16vWF51Hzcp+bF/Wle3J/mxf3Z9Jhvi+zp58ttsU7cFuvEbbFO9rQt1sSWYjd/B8yD+9E8uB/Ng/vRPLgfm89JZheFFfn1118xfvx4eHl5wdnZGX/99Rd69uypJh+RXub9+/ev95knnngCr7zyiqrH1rVrV6xatUp9noiIiKxXYzGfiIiIrBNjNxEROSqrS6KLo0ePYvPmzRg8eLCavbspEhISkJaWhmHDhrEmGxERkY1oTswnIiIiy2HsJiIiR2SVSXQiIiIiIiIiIiIiImtgVTXRiYiIiIiIiIiIiIisCZPoRFYmJSUFmzZtQkVFhaWbYje4T4mIiIiIiIiIqLmYRCdl165dGDhwIIKCgjB16lScbpWfvLw8REZG4tChQ8bn5syZg3bt2qFTp05Yvnw5HElz9+dDDz2EAQMGYMKECYiLi8OePXuMr/Xt21dNsKsvU6ZMgSNpjX36/fffo2PHjmjfvj2++uorOJLm7s8ZM2YgODhYzeR91VVXobCw0PiaI/+Otsb+dOTfT3uKkWvXrkX37t3rPW+p/y+tsS2W+l1t7rY01l75fGBgYK2fy8yZM62q/Sfb15Y87jLntrT1z8Fcv1fW9v+jNbbFknG+OdvCGGt/zPm3prq6GnfddRf8/f3h7e2N2267DVVVVXAE5o4/uoMHD6p96ShaYz+WlJSoOP7DDz/AUZhzP5aWluK6665T/6/DwsLwyCOPoKamBo7Ano5frJrURCfHVlZWpsXGxmp33HGHduDAAW3UqFHaRx99dFrrmDJlivwP1ZKTk9XjRYsWaZ6entrPP/+srVmzRouLi9Oys7M1R9Dc/blixQqta9euWn5+vno8adIk7eabb1b3i4uLNW9vby0zM1PLzc1VS0lJieYoWmOf7ty5U3N3d9fmzZun7dixQ+vSpYu2Z88ezRE0d39+8cUXan9u2LBB279/v9a9e3ft8ccf1xz9d7Q19qcj/37aU4zctGmTFh4ernXs2LHW85b6/9Ia22Kp39XmbsvJ2rt37161ffrPRBb5Hmtp/8nabsnjLnNvS1v+HMyxLdb4/6M1tsWScb4528IYa3/M/bdm5syZ2qBBg7Tdu3drW7du1cLCwrS5c+dq9s7c+9HUxRdfrHICjqC19uPUqVPVfnQU5t6PM2bM0MaPH68dOXJE27hxo1r3hx9+qNk7ezp+sXaO8ReOTuqnn37SgoKC1MGx2LZtm3buuec2+fMrV65U//FCQkKMSfQrrrhC/QfW/d///Z/6D+gImrs/165dq/3111/Gx2+++aY2ZswYdX/16tXa4MGDNUfVGvv0gQce0C699FLja2+88Yb2xBNPaI6guftz9uzZap/qnn76aW3kyJGao/+Otsb+dOTfT3v5+RYVFWkxMTHas88+W+/A1FL/X1pjWyz1u9rcbTlZeyXpNm7cOK0tNKf9J2u7JY+7zL0tbflzMMe2WOP/j9bYFkvG+eZsC2Os/TH33xr5O5mUlGR87aabbtLuuusuzd6Zez/qPvvsMy0+Pt5hkuitsR9lHXKx0pGSlebej6NHj9YWLFhQq7PntGnTNHtnT8cv1o7lXAjbt2/H4MGDjUOvZKhmQkJCkz5bXl6OO+64A2+99RZ8fX1rrfPCCy80Pj777LOxefNmOILm7s8hQ4Zg2LBh6n52djY++ugjNfRUbNy4EUeOHFFDkmSIsww9lH3vKFpjn/J39PT352OPPab2qW7v3r3o2rUrHP13tDX2pyP/ftrLz9fNzU0Njxw6dGi91yz1/6U1tsVSv6vN3ZaTtVd+LrLIzyQ8PBxPPvnkaZe3a832n6ztlvybYe5tacufgzm2xRr/f7TGtlgyzjdnWxhj7Y+5/9a8/vrrqmxGQ78j9szc+1EcP35clY/44IMP4CjMvR8lzt1+++0455xzsG7dOvVeR2Du/dirVy/1eyjlhnfv3o0FCxbg4osvhr2zp+MXa8ckugO58sor1UFv3UUS4FIrWic1Dl1cXJCbm3vKdc6aNQvdunXDDTfcUOv5goKCWuuUmlTp6emwJ62xP8W8efMQExODiIgI3HLLLcaDuvPOOw+rV6/Gn3/+iSVLlqgDP3vTlvuUv6PN359i3759+Omnn9TBnqP8jrbl/nSE3097//m6u7sjKiqqwdda+/9LW25La/+umntbTtZe+X84duxYbN26FfPnz8d7772Hb775xmzbcrJ2NKX9J2u7Jf9mmHtb2vLnYI5tseT/j7bcFkvG+eZsiynGWPtg7r81plasWKHqCU+cOBH2rjX2o8xBJfkASQA7CnPvR4lzcrFSLh7L39uRI0fi5Zdfhr0z936UC6hyEULqgvfu3RtXX301hg8fDntnT8cv1s7V0g2gtvP++++riRbqevPNN9V/MlOenp5qUgv549OYxMREdWIjJzl1ubq6qkl86q7Pnph7f+puuukmNUmr9PB55513cO+996r9bOrpp59WCQQJEvakLfcpf0ebvz9lcha5GCETisnVfuEIv6NtuT8d4ffTUX6+DWnt/y9tuS2t/btq7m05WXsXLlxofF5OHO6//341qdK4cePMtDWNt6Mp7T9Z2y35N8Pc29KWP4dTtaul/0ds7edyMpaM8y3ZFsZY+2HuvzW64uJiNano9OnT1UgLe2fu/bhs2TKsWrUKO3fuhCMx936cO3eumhDzyy+/VI/HjBmjegLfeeed8PPzg70y93586qmn1AVfmWxdEshyYeztt9/GfffdB3tmT8cv1o5JdAfSrl27Bp+X3rly5d2UzF4vV6Yaow83mjlzppqtt67g4GBkZWU1eX2Ovj9NyR8rCZqy/+TkRBK+dckV6rS0NNibttyn/B1t/v587rnnkJOTc9LeEfb4O9qW+9MRfj8d5efbFOb+/9KW29Lav6vm3pbTaW9r/h2Tdpxu+0/Wdkv+zTD3tlgynjRnW061Plv6uZwOW/m5MMbaj9b6WyPnBTJi9eGHH4YjMOd+LCsrU0leueDt4+MDR2Lu30cplzVp0iTjawMGDEBFRQVSU1PRs2dP2Ctz70e5CPH333+rGKWXhJMLZPaeRLen4xdrx3IuhIEDB6ohL7rk5GRV41D+4zQmJSVFDeeU2mf60Gp5TmovydDbuuuU3uqNDRWxN83Zn3qvOtl3OvkjJUNwhNR0lACqk/V37NgRjqI19il/R09/f4rffvsNr732Gn744QdjzTVH/x1tjf3pyL+f9vTzbYyl/r+0xrZY6ne1udvSWHult3ufPn1q9XpvzZ9Lc9p/sn1tyb8Z5tyWtv45tPb/EVv7uZyMJeN8c7eFMda+mPvvpnj33XexePFifPXVV3B2dozUiDn344YNG5CUlKR6UOs5ASG3kiuwZ+b+fYyOjq4V+w4fPqxG3Mloantm7v0oo48yMzONrx09ehTV1dWwd/Z0/GL1LD2zKVleZWWlFhYWpn300UfGGYzHjBljfD03N1erqqqq95nk5ORaS1RUlLZq1SqtsLBQ++WXX7TIyEjtyJEj2tGjR9Vr33//veYImrM/xa+//qo+t3z5cjUjd79+/bQXXnhBvXbrrbdqI0eO1NavX6998sknmo+Pj7p1FK2xT2XGatmPO3bsUL+z/fv311555RXNETR3fyYkJKh99umnn6p9Jos+A7gj/462xv505N9Pe/n56lasWFFvxntL/X9pjW2x1O9qc7flZO296KKLtEmTJmn//POP9tprr2murq7aX3/91ebtb07bLXncZe5tacufgzm2xRr/f7TGtlgyzjdnWxhj7Y+5/9bI+YGHh4e2ZMkS4+9ISUmJZu/MuR9LS0vr5QQkxSS38po9M/fv49y5c7XOnTtrGzZs0Pbt26ddfPHF2qhRozR7Z+79OHr0aO2ss87SvvjiC+3NN99U63788cc1e2dPxy/Wjkl0Mp58eXt7ayEhIeo/3+7du42vSSDcunXrKdch//EkYIqamhpt4sSJmpeXl1rkP7A85yiauz9fffVVdRIcGhqqPfbYY1p1dbXxD9+VV16p9qXs53fffVdzNObep0ICqru7u+bv76+deeaZDnHg3JL9+X//93/qNdNFD7iO/jtq7v3p6L+f9hQjGzowteT/F3NviyV/V5u7LY219/Dhw9rw4cNVUqV79+6tnoRurP3Nabulj7vMuS1t/XMwx7ZY4/8Pc2+LpeP86W4LY6x9MuffGvl9rvs7MmzYMM0RmHM/1uVI/TTNHcdnzZqlxcTEqL+zl1xyiZaRkaE5AnPux9TUVG3s2LFaUFCQSgJPnjzZYf6+29PxizVzkn8s3RuerIMMddm8eTMGDx6MkJAQs6zzn3/+UZO1DBs2rN4EYPauNfano2uNfZqQkKBqesrvqKPV+eLvqHnx99O+2dP/F3v6XW3utljL/63mtP9kbbfkcZe5t8We/o9Ycjv5t8v2fv/Icf7WWBL3o3lwP5oH96N5ME62PibRiYiIiIiIiIiIiIga4RizZxARERERERERERERNQOT6EREREREREREREREjWASnYiIiIiIiIiIiIioEUyiExERERERERERERE1gkl0IiIiIiIiIiIiIqJGMIlORERERERERERERNQIJtGJiIiIiIiIiIgcwAUXXIBPPvnE0s0gsjlMohMRERERERERERERNYJJdCIbt2PHDvTu3RvBwcF48MEH0aNHD7z99tv45Zdf0L17d/j4+OCiiy5Cenq6ev+ePXvg5ORUa5k0aZJxfYsWLUKfPn0QGBiIKVOmoLy8/JRtWLBgAcLDw1FYWGi8sj1t2rRTfk6++7777oOvry9mzZqFs846C9HR0Th8+LB6/bPPPkPXrl0RGhqKxx9/HJqmGT/73HPPoV27dggKClLtrK6uVs/Ltjz11FO455571Hpl3+zdu7cZe5aIiMh+Y/ett96qYqVu6dKlaN++PWpqak76OcZuIiIiInJETKIT2bg777wT48ePx4oVK/Dhhx+qZeLEibjhhhtUIvvAgQMqwT1z5kz1/m7duiE3N1ctiYmJCAkJwXXXXadek/deccUVeOCBB/DPP/9g48aNePnll0/ZhtGjR2Pw4MF45ZVX8Mcff+DgwYPqZLgpvL298dBDD+GZZ57BO++8oxIHq1evxt9//61OsF977TUsW7YMn376Kb788kv1GfmOl156Sd1u2rRJvf+HH34wrvP999+Hv78/du3apbb9+eefb+beJSIiss/Yff311+Pnn382Jrl/+ukntU5n51OfHjB2ExGRrbKGC9ni9ddfVxev/fz8VPwvKyszdki77bbbVLskHkqs1UnMlhjfsWNHREZG4s033zS+VllZif/+97/q+djYWHz77bfG144ePYqRI0eqC9XyXRUVFadsn5R7kQvlw4cPR0REBN577z11EfzGG29Ur0sHOrkgL8ck8fHxWLlypfGzGRkZGDVqlPq+Tp06qWMMney/xYsXq3XL6zfddFOtC+5EVk0jIpvm5eWl7d27V90fPHiwNn/+fK2iokI7evSoVlpaqq1atUobO3asduGFF9b6XFVVlTZs2DDtv//9r/G5mTNnameffbbx8f/+9z9t4MCBTWrHwYMHtbCwMC0+Pl77/vvvm/QZ+ROUmJioffzxx9qgQYPUc9ImeTxlyhTt+uuvN7730Ucf1a677jp1v6ioSMvJydGOHz+u/fbbb1r37t21Z599Vr128803awMGDDB+7v3339cuuOCCJrWHiIjIUWJ3ZWWlFhISoq1Zs0arqanRoqKi1P1TYewmIiJbNmTIEBU7t23bpvn5+WmrV69W8cnDw0PFsvT0dG3cuHHaXXfdpd5fXV2t5ebmqkXin8TO33//Xb22f/9+zd3dXZs3b562b98+rU+fPtpzzz13yjbIelxcXLSVK1eqz0kMfO+994wxVc6r161bp44H5Pt+/PFH9dqnn36qBQYGqngtr3l7e6tbMWPGDC0uLk7bsWOHer+0S87RxVVXXaWNHj1aS0pK0p555hkVy2VbT0Zed3NzU+3o1q2bapdst6urq3p98uTJ2llnnaX2wdtvv63aVVhYqF6T2D9mzBgtLS1N++6779R+luMOId8txwB//vmntnbtWs3T01NbtmxZs3+eRG3J1dJJfCJqmS5dumDdunVq2PT+/fvRs2dPdSX3sccew6+//qquCsvVbX3ItE56istzpj29jhw5gq1bt6qr6KKqqkpdHW6KuLg4nHPOOVi/fj2uuuqqJrff09Oz1q1pW6SHnt4WuVret29fdT87O1td/U9ISFBXsOWzpts3bNgw4313d3de2SYiIqtiDbHb1dUVV199NX788Ud1X3qgDxkypEntZ+wmIiJbtW3bNtXLWkZ59erVCykpKTj77LNVWbKAgAA1Wqq4uBiZmZnq/RIfJa5JzLryyitV72sZiS2++eYb9O/fX/VAF3fffTc++ugjPPnkkydtg4eHhzFOynn0hg0bar1+++23q5HeQnp+Sy95Ocf+/PPP1Wty3i3Gjh2rjhvOO+889drUqVNVr3hZzjjjDCxcuBB33HEHfvvtN3XcIb3C5VhCepU3xYABA1Q7pHf7uHHj1P6S4wwp/SYjzWREmxzT3HvvvWr0nIxIkx7o7777rurRn5qairS0NNVrXXqnd+jQwbifLrnkEnVf2invI7IFTKIT2TA5wZQT7/vvv18N+ZLg1a9fP3VQIMlsORCQE2kJYqbDuWQotQwd37Jlizpx1klNUwnEr776qnosBwolJSVNaosMIV+7dq0aWjZnzhxVL7UlpC0S8GWInT48Ta/TOn36dBWAly9froaDyZB0UzIcnIiIyBpZU+yWId3SBklaSyyVmNoSjN1ERGTtrOFCtiTOpYyZXsLtsssuw1tvvYWwsDD1up5sFlFRUdi3b5/x+9asWaM+K6QEjCT29dckia7PTSbHAlIaJisrS7UrJibGeFFAyr00henFctP7sk65ACDHEXoZuKKiInVBQkhJt0ceeUTtC0nwC9P9Ke3S8cI52RIm0YlsmEy6tWrVKhVI5aq5HmzlSq8EopycHPW6TOQlV9qFXOWVumP61eG8vDwV+OTkVa4uS101OZiQOnEvvviiqlkqV+NPRoKyXBGXem1yFf/iiy/GNddco2q8NZe08T//+Y+qGSsn5VLfTU7Ev//+e7V9EsSltpvUU5VedHKwQ0REZO2sJXbrJ7HS207Wu2TJkhZvG2M3ERFZM2u5kC311mVUlnREy8/Px7XXXqviviTSxaFDh4zvlWMAqUmuf98tt9xinBdF6q9LElp/7dlnnzWOKistLVXHCVKz3MXFRX2n1FiXfdDSnt+S7Jfe9AsWLDAex8jxhKxfEvsy8kx6v48YMULtjw8++KDW53nhnGwVJxYlsmFyci2BSoZAy9VsLy8vNVnZzTffrK4uy8npjBkzVK8wmYhMAppcFT5+/Li6aiwTg8giBxKic+fO+Oyzz9RkYXKFXiZd+eqrr07ZjjfeeEMdMMj3yMGAHATIgUlLDB06VLVdTsZlO+RKtxzM6L0ApG0y2cpff/2lEgjSA4CIiMjaWUvsFnJSLRe9ZXK1gQMHtnjbGLuJiMhWLmQnJSWpibDrXsiWEiiS0NZ7R+sXsmXyUf1CdkFBgXpNYpmsTy5kS1JZ3jN58uRTtkMm0ZZyJtIO+W69Y5pOks7SW14uis+fP1/FaiHt+Prrr43tlY5sMgpcf00m9JZRYJJAl5JtMqGnm5ub6uku8VmS8y+88IIqsdISciFfysxIO+W+9Ey/8MILVVkaif2S3JekuuxvubAu2Nuc7EKbVmAnIrP64IMPtEsuuURLSUnRsrOzteXLl6uJOfLz8y3dNCIiIrLi2F1SUqIdPnxYu+2227THH3+8Tb+biIjIEmSS0P79+2vBwcFqYk+Jv3fccYeKwZdeeqmaqFMmzZ4+fbqa3FMm+5YJNiV1ZrrIZNy6hQsXar1791afHT58uJootCmeeuopLTIyUk02ftFFF6lJOIVM4Hn33XdrsbGxWmhoqHESbiETgb/44otahw4dtICAADUxd3FxsXpNJih/5JFHtHbt2qnPPfzww2pCcpGamqqNGDFC8/f316644go1AXlTJhaVtuhtksfJyclq+0VBQYGaXFT2pWyHtEv3yiuvqOejo6O11157TbX1hx9+UK/J52U9On3dRLbASf6xdCKfiJrn4MGDahiaDNmWq81Sj1yGpD3wwANm/R65Aj5mzJgGX5Or3y+99JJZP0dERGSvrCV2y/dJL/bu3btj8eLFaNeuXZM+x9hNRES2SkqySJkW6UHt7e2tRkjJRJjHjh2zmhIjUmpNyqHIQkTWhUl0IjolGUouNUwbIgcbMgzcnJ8jIiKilmHsJiIiss4L2Se7IN2WSfS7775blYtpiJRe69+/f6u3gciWMIlORERERERERERkBrZyQTo7OxtFRUUNvta+fXvjpKVEZMAkOhERERERERERERFRI5wbe4GIiIiIiIiIiIiIyNExiU5ERERERERERERE1Agm0YmIiIiIiIiIiIiIGsEkOhERERERERERERFRI5hEJyIiIiIiIiIiIiJqBJPoRERERERERERERESNYBKdiIiIiIiIiIiIiKgRTKITERERERERERERETWCSXQiIiIiIiIiIiIiokYwiU5ERERERERERERE1Agm0YmIiIiIiIiIiIiIGsEkOhERERERERERERFRI5hEJ3IgFRUVlm4CERGRTaiurq73nKZpFmkLERERnRpjNxG1JibRidrIn3/+if79+yMvL++U7926dSuefPJJ4+OCggJUVlaq+1VVVXjsscdw4MAB42N5/VQHB7NmzULPnj1RU1NzWu3+559/kJGRYXx85MgRfP/998b2nEpWVhY++eSTJr+/Kftm6tSpzToYkrbrysrKMG/ePBw7duy01yPff/PNN6O8vPy0P0tERK1v48aNmDRpUrM/L3E1Li4O33zzTa3nn376aYSHh6vXU1NTkZ2dDXOSWP3LL780+f3z58/HHXfcUetieUPxVmKmPN9Q7Lzpppvw008/GR+XlpY2mISQ44emXIz/7rvvcP/99+P48eP4448/cPXVVzc5Xv78889ITk6u9dy+ffvw9ttvq7jdFIz1RES2yRZjt6zz//7v/7B79+4mf+a///0v/ve//xkfS5xpLPnfUNzNycnBBRdcYIyXEp9lHQ3FeFmv5AtORfbRiy++qO4/88wzmDFjRpO2Rdb/3nvv1Wvnb7/9ppamMo3dR48exZw5c9AcI0aMMG4HUWtgEp2oFRw8eBBpaWkqgSxBWhY5gdu+fbsKcPpzskiQkGBeN4i89dZbxscxMTFwd3eHq6srPD09VWDo0aOHeuzm5oaAgADk5+cbTxgliNVNlktASUpKUie0DZ0Ul5SUNLgtjzzyCC6++GJjYN+8eTOuu+66JifjZdsmT55sDOpr1qzBXXfdhYceekitW1/k4OP2229XByJ6u4qKiup9jzz3yiuvqPWYkvYVFxc3epAg+33QoEHqAEE4OzurAwTT/dwU8j1fffWVuvXw8DitzxIRUdvw9fXFl19+iQ8//ND43EUXXQQnJ6cGl7qxUS7+StwYPnx4redDQ0NVnPH398cDDzyAa6+9tkUXievG3q+//lolEUwTyHKy3Bg5hti5c6fxscRROV6ou30S8+T5hk7yt23bpo5XdN7e3ur4ou46XFxccM4559Q61mnoJPvzzz9X+1OOTbp27Yq1a9fWSvQ3Ro4TXn31VfznP/+pFfvlQoHEa9M2NoaxnojIdtli7Jb1vvnmm0hPT6/V6etkF5337NlT6/y/e/fuDcZdiWHt27dvcB0rV640bsPff/+tcgTy/rrrkPVKBzzdwoULVVw1JdsgSevExET1uF+/fnjuuefU/jwVyWc88cQTmD59eq3nJZ7Pnj27SQl8OY7p1KkTFi9erB5LvL/vvvuwZMkSnA7Zr8uWLUNISMhpfY7odLie1ruJqEluuOEGlbD28/NTwUvv2SXk5M6UJL3lBO3w4cO1TmC9vLyQm5urgqOc9Mp65Dm5DQoKwu+//47Bgwerz0twkpNVMXHiRPzwww+Ntm3s2LENPn/mmWdi06ZN9U6sJSgvWrRInTwL/WRSf6wHOjk519sgxo0bpw5Y9BNnSX5LOyW4Sw8BWY+sVz4rPb3kRFW2RV9vSkqKep+s18fHR31OyDokMF555ZW1TrzlYEY+v2LFCnVlvi45CJALGRMmTFCPZb0S8PWeZt26dav3GTm4kcS8vFf//uXLl6sLJPIzPnToUK33SzukffKz7tu3b6M/AyIial0y8krioYymuvXWW2v93ZcLt6Y6d+6sTj51EkteeuklvP766wgODlYXd+XEW0ick1gs3n//fXVRWE4gIyMj1XNy8i6xa/To0aoHtMjMzFQXwyWx+/jjj9f6bolXcrKqv1cujMuimzlzpkpC7927t1bc1ck2yaJ79NFHVS9wOU7Qjz8kNhUWFqqRcLGxsadch5yYS5yV2KuTYxH9Yr1OEgYvv/yyipOyv4Qcy8gJujyWE3dJosuFAb03t8RIWZe+D2VfSzJeP/mXmCwn3rK98h55v/TWk5NpOU6QXneSmIiOjq7VPh1jPRGR7bJU7JbRWNdcc406546Pj6/VCU3+3v/111/G5yQWSgySxPktt9xijNmmsfuKK67AhRde2GgSum7clZ7zcv4vuQOdxDz5Lj2HUPfzprdyHCEdzOTc23S98llZh2lSWeKr9ISXCxaXX365ek7itFywlwsM4qqrrlK9y4cOHWocyWb6fXpnQDmfl2ONO++8U+1vPV7u379f5RAkXyGdA+XCuPx85Ofb2Cg82af6xY8+ffqoCx0PPvig6sDX0MVsSZjLcZF+8UG89tprCAwMxPnnn18vdksbZDvkM126dGmwHURNohFRqygpKdFqamqMj3/77Tfpil3rPdXV1VpxcbFWVVVlfC4mJkZ7/vnntXbt2mk33nij9sADD2j/93//p7m7u2t+fn5aQECAWo+vr6/m7++veXh4aLfddpvx8xkZGVpqaqq2ZcsWLTMzU8vKytKCg4O1t99+W93/7LPPNBcXF3Vf3puWlqYlJydrhw8frrcNN9xwg9arV69azy1cuFB9f2VlpfG566+/XuvRo4faFp20Xdq9c+dO9X5pu6enp/b1118b3zNp0iTtqaeeanD/yb47evSoVlZWZnxOtqt9+/baN998o1433b8HDx7UUlJS1D6tq6CgQOvQoYN266231npetqFfv37aoEGDtPLy8nqfk/dL25uzEBFR25N4mp6eruXk5KjYlpubq2KAuPzyy7XnnntO3f/nn3+0oqIidT8kJERbsWKFcR0Sl84//3x1/5NPPlExdO/everxxx9/rOLzsWPHtD///FN79tln1XvnzJlj/Pwrr7yieXl5adnZ2erxjBkztMDAQC0/P79WW6WNzs7O2vz5843PnXnmmdr06dPVfYln0dHR2uzZs+tt59q1a7U1a9ZoU6dO1QYMGKAer1q1qsn7Sb575cqV2rp167T4+Hjt6aef1lavXq3t379fOx3Lli1TxyEzZ85Uj++9995Txsdzzz3X+Plt27Zprq6umo+Pjzq+0Rc3Nze1mD4nixxLyPf99ddf9drCWE9EZJssHbsl3nbr1k274447jOvTz2EXLFhQq60///yzit1HjhxRjwsLC9X79Lbs27dPPZb4Wjc3ILFL4vWFF16ozt8ljsv3NFVSUpL2999/q++S7/jpp5/UYzmvPx2Sa5BYunTpUnU+3bdv31PGuyeeeML4+TfeeKNWbkJfnJyc1PFP3dgteQuJ9Q3ZtGmTyk18/vnntZ6X3wM5NpB8QkM6d+7crLg9bNiw09pXRHXx6I+oFZSWlp7WH3M5IBCS9JbHkmiWQP/ee++pgC7B2TRpLe/RT5Yl8FVUVNRKYMtJYteuXbWJEycaDzLk4EFIsJVAJclpCSJy4twQObmW77n55ptPmkR/6623VMD88MMPa73vpptu0h566CHjAYip5cuXq/Z0795dHUDIfVlk3aYuueQS7YwzzlAHI7KN8li2Sbbvtdde0y644AItLy9PfYecOI8dO7bBbbn99ttVAJeLBnXJCbwE+2uuuabeybX8HOU5PTEvbZQDDkkyyLok0XH8+PEGv5OIiNqenHTVjbFXXXWVeu2KK64wnohLXJw2bZrxvn7y+8svv6iTNokrcgLfs2dPFad+/PFH9Tf/7LPPVuv09vbWhgwZot11110qHkks0ckJvqxTTlIldkVGRmpPPvlkvbbKybusSy56N5RElwS1vF73xFJERUXV206Jc021ZMmSBo9H5ML96fryyy+1Rx55RMVGOamW9Wzfvl0lQSTJII+lI4HES0mSSBKjbvLE9IK5kMT+Cy+8UOs5Od6RmNxQIlww1hMR2SZriN1z585Vr+t/76dMmaL17t27Xluvu+46Fdf1ZH7dJLok8+UisHTwMiUxpaG4K9vXVJLIbmgdcn5/uuSi9w8//KB9+umnah1yLi1xWxbJRchzcl/yE9LpTs65TUnclGMcnXR+k5zA+vXra71PYqvEWdNOg6br6NOnj7rIbdo5Tvf++++rdrz66qv1XpPch3y/HrslZyGd+mSdcgGjoc8QmQOT6EStQIKABBu5mt5QMNIX6aUmvaf1k0dJLktPawnCkkSX98hzeoCUq96SADdd5Dl5TXqN6x599FHV61uu7DaWRBdyUCGf1Q9GTK+UywnsqZLo8+bNU98vV6NNSfCSJPrDDz9cK4munyiPHz9eXRyQNuuLXAG/6KKL6rVDDiwk0SAHPNKjQJL+kpyX7ZMTd1lfbGysOnk2DeQ6OaiS75ce+I35/fff1Qnz0KFDVW/3hsgBRGhoqPEgLiEhQa1Xfn5ERGQ98VdOrORWTqSlB7j8ja97Ii4nwB999FG9E/HJkycbY670mpKYLOv5z3/+oxZJMtcdjSXkPabJWenlJoluib1yYq/3SjclJ44S1yTe79mzp14SXWKj9ESXxPR3331X67O7d+9WI8ikJ5bER7kvyXhZ5LXExMR6y65du7TNmzerz8vJsDynx8jXX39dJTHk5HfHjh0Nfl4WeU3eV5fsD4mhMkJO1qe/xzS5oJ9I1913+nFFUxf9Z2iKsZ6IyHZZQ+yWc8qIiAht1qxZqme3XHitG1PkwqokyCV2n3feeSqRbhrnZP0StyUhLW2QGKKT+KfHYXm/nM8eOnRI27hxo7rwLO9tKO7KubTeo14uTB84cEB77LHHjL3dpXe69GiXdTf0eVnv1q1bG7wYLOuTmCexu2PHjvVG0MvPQ87fTTvrmeYbTid2NzRaTvaBXLiQdp7qwoFc+JB92BDJl0hOQjoBinfffVfr1KlTo+skagnWRCcyI7kwJbU4paaZXoNNp9cylbpdOrkvNb6kFqfUK5W6p2eccYbxdb2ml9Qok/U2pmPHjsY6nnPnzlUTj3766aeqzrleA0yfpEvqukmNNWmrTLYltcRl0g+pCSc1R+X52267TdVjN607Xtc777yDhx9+WE3yqddP00k91s8++0y16Y033jDWK5PvlXpzUsd05MiR6jXdCy+8gNWrV9daj9Sukxp1UudM6p5JbVWZeERm+pbacaNGjVL7TyYhkdfqWr9+PW688Ua1HVK3VCZL0Wum1a2pJ/tNJjvt1asX3n77bdx0003G16V+mkymKhO7yIRnUttNn9VdarqbrlO2TWrREhFR25O/xxKDhcRCqQEusaIuiUvh4eH1npea5VIrVWqinnfeeeqx1CiVmCYkBkkd1B07dqi5O2TyMJmLQ+KN1EzVJ96UeCrxUeKhzA1Sd5KrX3/9VcUkWWRi7csuu0zVgNVJzPv+++/x559/qvvjx49XMfXqq69Wr0tdUTk2kHriUkdUarFK3VOp3yo1YiXmSuySeqj68Yg8lvdJbXSpDyuL1C8X8h6JuVJ/XGqj6nVO5fhE9pVed1ZqmsrkpXUn6pS673I8IXXfpW6t1IU3ZTrJm8TwMWPG1KoLL/XH9XlfZF8/++yzqqaqaXzV65nqdW11jPVERLbNGmK3xD05p5VzXJl7Q+qLS+w1JTXXJb7JZ4cMGaLmADOdpFNqoEssltgsxwFSF13mGZH5OCSOSuyRc1sh7ZMa4wsWLFDn4vL9Euclbsv5uL4/ZE6Riy++WB03REVFqecSEhLUrcTDiIgINfmqxGG9Lru0QeKUHAvo9chlsmw5DzcldcMl3yDbKROJ1o2den5BSK5C6qjrpk2bpo5f9OMDOTaQtkoOwpSc/8v+lDr1pmQ/Sw1z+V6Jv43FbqmRr+cKZKJROUbRcxz6vCbS/smTJ6vJSSV2S1uF3DclbdD3K1GztSgFT0S16Feim7NIvU7pASZXsfWe6Dp5Xt4j9clNF723uN6DTchVaBmeZkqupMsVWSHDwuUz0stbyBVmueKu95KTdckVaWmD1AltrCe69IyT4V8Nkavw+pV+Gbom9dH1K8fyvKxTegTIlXi5Si8956Tm6+jRo+utS+rWSY92IWVvpAfC999/r3rZy325gn/11VeroXXS818n3ye93aXmnJSaOdX+v+yyy1TPOSkRY7o/5eq7tL+pP0ep805ERJYl8UV6i0kPa733k2ldVYlhehkV6c0mpVNMSRy99NJLjY8l3snQbikxpg8Jl+HHUj/1f//7n+oNVrckiV4fvO6cIxJ/JT7p85lIjzeJZzKsWnqiy1Dw4cOHqx7TOql9LrXETYdDy/fqQ7ClPdID3XQ4tIw8k207GdkGWYf02JL6sHVHdEkPQD0GN0bisL5d+uizuuVcfv31V7Wd8ti0R5uM2pPnTUfpybHAlVdeWes500V6i0tZGMFYT0RkPywdu2WUltT5llHbdc+nJcbK98tIaL33s5yvS0kYWbfM2RUWFmac70t6uEvclh7bpmSUmR53u3TpYiwLo5NzZCk91hhpo4yq0tch31e3h72UNpPe5CejjxiT2C1zpzVUzkXOrSVGy3GMfnwhxwkSu6Vnu/5+uS+982VOmIbitqxHyr3oveFlX8qocil115R5SaS8m/ysL774YmMOQ0h5OBnV19TYLbXyiVqKSXQiM5LgIgFJAoUEGAloMuRKAqwMJZOJSaQOqZ5klvfIkGIJKPrkJKJuEl2GYDV0zUuCkumwaZ2sV04w9eAmwV2GIst9+T5Zn15qRhL/dekTudRNosuJsgwbl++UA4eTkeFvMtmZJLv14XNS412S4noSXT/okPbXTaJLUJSDHjkIkOHwMrxdDnykTIwk/e+//351ICMnzrKP5btkGJo+3E3IPpDtk/0hBzJy4i612kwnNxX65C51yUGNJOhlKP6ECRPUhQ79Zycn39J2+bnpz8kEq6YT0hARUduTkzsZTi1/o2WuDCmrIiedcmItJ+J6XVKJgfqJuOmcHBKngoKCVGyR+CFJ2xdffFGd7EnZEDlhkzqdQib0koRq3VgqxwNSS1W+Ry+nZnqx+5xzzqk1tFqvNSpJdEmYS4yU0ik6KYWix2b9saxfTthlfpORI0dqo0aNMq6/bhJdYlbdYdAyhFyGP8uF82eeeUad0H777bdqe2Wpm0SXxHhDJCFw9913q/t6Er2hci4NkWOd5nQ8kCSIjrGeiMj2WUPsFhLPJEld9zWJj3qddp20T49zkqCVdZomeWVCcdOL23I+LuuW2P3ggw+qGPjSSy+pdegX3E2T6KY123Xyfvm8fKdsn3SWk8/KdusXBPQkusR903NjnbRJ9vVXX32lHksSvaFyLg2R0ivN7TRoekFB5jwT0mb5eS1atEi9R5Ltpsc6Up5Hv3BhSmrOSw10mWNNOgFIbkCP0/Jzluf1x7LIvta3l6gl/h2fQUQtJkOQZPiZDPGV4VT79u1Tw6+k3MoHH3yAbdu2Yfr06WpolSwyfOmss85CRkaGcXhWQ/RSMLJ+00UfSmxaIkbs3btXDUeW4ePynv79+yMmJkbd79ChgyoZI8OZ5PF3331X7/tkeJkpGZolw9d69+6N9PR09dzQoUNPui9kOJsMK5ehaUKGkj355JNqSLTp/hLS1roOHDiANWvWYMOGDZg0aZL63rFjx6rPyBC9Pn36qGHnMgxN1v3333+rIfOy/3VSokaGncnPQobLyZB4uXgonzUlw8hlOHzdoWcyNE5KxcjQcxmyp+9rWfSfiV6qRhZpW92fBRERtR35Oy/DsiX2Sgy899571XDnpUuXquG/UvpLhm5LXJASKlKuQ0p4tGvXzhh7Lr30UlWaROKzxDAp+fHf//5XxezLL78c5557riq/Ju+94YYb1PBwGU5s6pdffjGWYZFh2npJNSHDuSW+yRBoKbEmpLSKToYaS7kzPVbJMGiJw6axWWKsDBe/88471ePHHntMbZscd0islmHbOhnWffbZZ6tSKzppz9SpU3HVVVepYxRZpESbbK8Mse7evbsq5WL6fbI/tm/fXms7ZT/98ccftUrRiYKCAlU2Rr5bFBUVqfIzR48eVUPVdbINsg8kNstwbvnZSew90dGnwUXet2zZMuM6GOuJiGybtcRuERYWpuKzaekSId8nZdYkhukx3TR2Sxz9+OOPVbv1Eip6aTTdE088oWKTlGuVMmpSEkXOyeV8XEqOmbZHtlfO4SXG6uQ8WsqaSFkyce2116pSZhJjb7nlFrWdpuQYQfaXaTwXUkJVSp2YlkWRNkvclkV/v5R3PX78uDoPl7gqpDSN7Ge9PKyUx5FtmDlzZqNxW9YtxzFSSlanl2KVfSQ/1927d6t9r/9MhZSokWOEurFb9pO0XdYrvyPyOT02y6KXodEf6zGbsZvMokUpeCJqlEx6IpN0yJA06dEtpNeZ9Kb+4osv1DA1ubL65ptv1puNum5PdNPJORvqiV53giy5ais9saTnuPQwkwlATcmVann+lltuOek26D3R77nnHtXr+/nnn1e96fWe5Y2R3m2y3dIuve3Se1yGicnnJk6cqK6yL126VLVDSE90GWatkyvy+gQzpmTYnaxPhpYJ09f17W6MDHeT/Vp3nb6+vsZJakxJj335Pv0KvfRO0+m9IWSCGdPeeLKviIjIMiTuSGyTXk36pGN1J9OSEVXSy0v+hjc00dbMmTNVL27pCW5aJsy0h5YM95YJyKTsSt2h2OKss85SvZqlB7PEw/nz59d7j7Svqb23ZEi4aS8wOZaQHmgSm6Qnuk5irZRJkx7Zpj3RJX7L8HSJz2LGjBlqGySWSe93vbe8xEfpvaX3Ptd7osv6pIycDIk3LSkjJVSkLXoveT3my7qlJ5ws8lh6eUuslWMJfbK4umTSMHmv9CCXdklvdtNFnpNe9nVjeF2M9UREtsVaYreQ0c8Suxqix7imLjIppmlpGTn3l0lAZTv0ScTFJZdcYux9btoTXZ6XHvnSo10fUdW/f39VhtV01Jcca8jIMr2Ei94TXc75paf6fffdV2s7ZIS8TIyqk9gnI7j0uC0xW9YvuQy5L+1uqNe+tOvss89WIwCkFI9Mclo3dktPeGnHqUgJOxnpZUof0S69zuuSEnh6r3XZX7LfdJL7kGMZU3JcUHeSdqLm4KUYIjOTK7tPPfWUupUr3zJJhk6u0spkKVOmTFGPf/jhh3pXjM1Brr5KTza5Iv7jjz+qiVnk8YQJE5CcnKwm9Rw4cKDqyd0UcsX7nnvuUZO1LFq0qMH3yAQeei85Wbf0KJCrxnI1W8gkYdIWuQIs75N9IftIrrDr5Iq9TiZzkSvWjZErzo2RHgL6JCemPdBkEjGZdMa0R4D0lpOr99JDvy7Ttpn2ImxMU95DREStR+LOhx9+WOs508msZEIvmUhMJqeSGGw6aZZpTzFx+PBh1eNZ3i89vWSyMj1WScyTOCUTgkmvM+lppccWmQx006ZNahJLGWUmPd5mzZqlJiAzjT8y0Zn0vpJezvrzMgGn9KaTnuVC1iux0LSd0lttxowZquf4nDlzasU+iXMy0Zf0yDYlvfGkN7mM2JIJTKVHnfSG69KlS633ycRl0jtc1mFK1iffJZOAyuRs0gtOYt7LL7+sesLVHcEmPfxkZJjEV3nt999/V59tiPQsv/vuu1UPPrmVScTk+/TJUuX4Re5LrzfZRlln3RivY6wnIrI91hC7m0JGaUmckd7T+kgl6bUt7ZfRTPp3SbyS2C2Te+pkhLYcF0iPe5lQUycj1WW7pL11vfvuu+jRo4cazS0TekuMlNFm+khvnUxkLufpo0ePrvW8HC88+uij6jxceuZLL33ZPzI55/vvv1/rvTJqXUZ4C4nZMgJcH03WkGPHjqk8hhwzSG/3wYMHG9sl8VZ62su+ldF0MlpNRuA1Rn4eq1atUsdPpmRfyzoaGrGvT7QuGLupLbGcC5GZyQmoBGgJhHISrAdmGQolSWw5EZTh0xJkZGizBJamaqycy8lI2RU52JDhXBI8JcEtAVQORuqeZDcUbOTgQoazSWA+GQlkpifdMju2fFaGtAspYyPJe/2AQA4C5IRc9snChQvVEDSZ6dx0uJoc7Oht0Jd169YZX687VEyGezV0ci3DzC655BJ1MCbD5uqWvhENnVibOllCf/ny5arEjBywnSy5T0REliMnehJ7br75ZlVSxZScvN9333344osv1AmqnPhKgllKmUmSNyIiQg1flsS1lGiZPHmyOhHVLyxLXJeTY/H888/jwgsvNJY4kc/s2rULP//8c63vlDIkcjIvsTwwMFAtclIuMUx/LK/Jd4eHhxs/J8cRjzzyiDoO0IdX6xe8JW7qJV5MX5ML2BL/ZN1SQk5iurTZ9H2ynbLe//znP8ah06brGDZsmBpqLyfOYv78+eqC+e23397kn4GeCJdbkZSUpIa1y1B2OV6Q4wJphyQl5CK8HCt9+umn6r4cW0mcbyyBzlhPRGR/2ip2N4XEX71kqh6n9ZIucr6sPyevy/tMy73IY/n+urFVzp/lArqUXav7miTBJSZLDJRz4muuuUYl8k3fI2XcJHZKslzPO5i+/sADD6jzcom3Qjr5ycVpucDfFPo5tuxHnZRQkWMcuUAt5+ZyPCA5DYnTckFAyPdJu+VzJ0ugS0yVfIBckJcYXjd2S3mXU+UsTha75QK9dAaU3AFjN5kDe6ITmZn0NJcgp1+dlsAhJ89SP0yCqySyP//8c3WCd9ttt+HBBx9UJ5ByVVqCn9Drg+r0+6bPCdPvaYzUEpOaZxK4pY6brFsOLqRN0vvqZJ+X9de9aqtfYZbEt15vVGqcSq9yuVKut1/qi8oJ/caNG/Hee++p3m86CarSC05qmUm9czmAkdrnEoDlIELU7dXWFLItplf8JZjLvpaRAXLQIwFfX6/UUJdtkDpzksSQg5TTCc76fpGfiaz71VdfVT/DW2+99bTbTURE5qdfhBWS7JWeUvL3Wk6u9eclMdq3b1/VC0oudktPNolfcuFX3i8xQ04KpXezLEJ6SkmSXGKp9C6XE1HpgSY93iS2SG8q0xqmEmvl/ZJcl3gnF9AlLktbZKkba+R1qdlqSuKqvCbJb9OTSYlz+sV4OZ6QNkviQOqOyraZ9uSTeKufwJvS1yFtkeMEiYnyWYmZEtsHDRpkfK9su36SnpCQoE6ipc6sTt+vcXFxtb5Dj+06OemWnuqybom/K1euVNsoo/dMewXK+qQWq/z89MdyoUBOqvX5VBjriYjshyVid1PIhVrpmS0x2PT8WU8uS6/purFbj889e/as97z+mlwIkPi3ZcsWNWpbOuTJBWudvF63t7z+WbmV2uLSc1zm9/joo4/UeiS5rcd/SepLol3WIftWEtNyLCCJdJ3sV7m4UPd7TB9LDkDiryTJZcSc5DQknyH7WY/RQtYjZF9I7XrTC+hyDKOfq8vPTUa2Sb5AciFywVwn92W0ntyaHmOcTuzWf1fkeEsussgxkMRvohZrVhEYImoymTVaaoA+++yzaoZsUzIrtcwkLbXOpD6p7scff1Q1yHRbtmypVxNdasa9++679Wp1rlq1SnvhhRe0m266SdUvlfpmUsN0zpw56vulVtwFF1ygnpfZwXv37q2NGjVKe++99+q1fdy4cdq1115b6zmpeyo1xurWfJMZvfXZv+X75bmLL75Ytd20bprMWi612WR2c31/SC3z//73v6reWmO1UnWrV69W6z527Ngp973MwC3beeONN9Z7v8xiLjXfRowYoWrTna6EhATVDpllXequmtaIJSIiy5Pap0uWLFF1M6Vut/zN37ZtmzHuSGzUY5jEpYULFza4Hnnv1VdfrWp2162z+sorr2ienp7ayJEj1fuaQuZFkXgn36nXHz3VIu2XmuZ6/VPTGuhS07wuqaE+evRoVVf2VLp166bqoZqSuNizZ0/tzjvvbLC2rOncLHXri8v+lNrrcmxSd5G6rVIzVY//chwkxzMS25uyP+TYSI5dZL/rGOuJiOyHtcbuKVOmqDlOpEZ4U2O3fEenTp3qrUtqnetzj+gkznTt2lXVK5d5zU4mPT1dbb/UaDf18ssvqxrlcmzQ2Pwhsr36PCY6mastOjq6wbgt3yU16yV263bv3m2cb6Up+0N+prLvpMa5To4v5OcnORI5DjD1+eefq9r1MrdMWlqadrokR6Lv95PN40bUHE7yT8tT8UTUGBm+JFdupYTKyciVZL13mdQpk6Fd0sNbSGmYIUOG1OuJLqVa5Gq6DFPSbd68WfV0k95dUn9UrsY31PNKhnPLFXuZpVx6k8kVWqmh3lRyxVdvj1ylrlubTa5yy3CzumTItlzFlqFpdc2bN0/VQpUadScb8iU15eQqt9RuOxW5Cl635qs5SP066YG3f//+Vlk/ERG1jMQlKWcmPY9kbhAZFWVa/9pcJB5K/D6duqr2SkaVSV1UmX9Fepq3FcZ6IiL7wNhtmZH0MleaXhO9LUjve+m9fzr5h6Z64403VJ146YVOZG5MohMRERGRXTvdycOIiIjIshi7icjaMIlORERERERERERERNQI58ZeICIiIiIiIiIiIiJydEyiExERERERERERERE1whUOrqamBunp6fDz82O9LSIisipSca2wsBDt27eHszOve+sYu4mIyFoxdjeMsZuIiGw9djt8El0CeYcOHSzdDCIiokalpqYiOjra0s2wGozdRERk7Ri7a2PsJiIiW4/dDp9Elyvh+o7y9/e3dHOIiIiMCgoK1AmnHqvIgLGbiIisFWN3wxi7iYjI1mO3wyfR9aFkEsgZzImIyBpx2HNtjN1ERGTtGLtrY+wmIiJbj90s0kZERERERERERERE1Agm0YmIiIiIiIiIiIiIGsEkOhERERERERERERFRIxy+JjoREbWt6upqVFZWWroZVsHNzQ0uLi6WbgYREdFJMXb/i7GbiIhsAWO3+WM3k+hERNQmNE3D0aNHkZeXZ+mmWJXAwEBERERwAjIiIrI6jN0NY+wmIiJrxdjderGbSXQiImoTeiAPDw+Ht7e3w594ysFNSUkJMjMz1ePIyEhLN4mIiKgWxu7aGLuJiMjaMXa3XuxmEp2IiNpkKJkeyENCQizdHKvh5eWlbiWgy77h8HAiIrIWjN0NY+wmIiJrxdjdurGbE4sSEVGr02uxyZVwqk3fJ6xXR0RE1oSxu3GM3UREZI0Yu1s3djOJTkREbcbRh5I1hPuEiIisGeNUfdwnRERkzRinWmefMIlORERERERERERERNQIJtGJiIiIiIiIiIiIiBrBJDoRERERERERERERUSOYRCciIiIiIiIiIiIisqYk+i+//IJOnTrB1dUV/fv3R2Jionp+165dGDhwIIKCgjB16lRomtak9X3//ffo2LEj2rdvj6+++qqVW09ERNZgx44d6N27N4KDg/Hggw+iR48eePvtt3HBBRfgk08+wWuvvaZiwx9//GH8zN9//63ijsSZCRMmIC8vTz0v75fP6Q4dOmSceOSZZ57ByJEjMWzYMAQEBGDcuHEoKCiwwBYTERHZNsZuIiIi28LYbcEkelJSEiZPnowXXngBaWlp6NatG6ZMmYLy8nKMHTsWZ555JjZt2oSEhAS1c09FEu833ngjnnrqKfz55594+umnsXfv3jbZFiIispw777wT48ePx4oVK/Dhhx+qZeLEieq1999/H0uWLFG3gwYNUs+lpqZi1KhRuOeee7B582YUFRVh0qRJTfquRYsW4dZbb1XxSQK9xBwiIiI6PYzdREREtoWx24JJdOl1Lgn066+/Hu3atcNdd92FrVu3YuHChcjPz1dXMDp37oxZs2apH8ypfPDBBxg+fLhKxPfp0wf33nsvPv/88zbZFiIispxt27bhuuuuQ79+/dCrVy+kpKSoK91CArWMerrssssQEhKinvviiy9wzjnn4LbbblOjof73v/+p9xw9evSU33XuuefipptuQteuXfHoo4+qzxEREdHpYewmIiKyLYzd/3JFGxszZkytx9JrXHbO9u3bMXjwYHh7e6vn+/btq3qjn4p8Trr7684++2w8++yzjb5ferzLorO2oQFERC0lQS07O9ss6woNDUVMTAysUZcuXbBu3TrVxv3796Nnz561rpa7u7vXer9cEZcgrouKioKHh4faX3WVlJTUetyhQ4danzt27JiZt4bIsTnK3y1r3WeOtN/Ishi7iewndkheRf4/mgNjEJH1Yuy2YBLdVEVFBV599VU89NBDOHDgAOLi4oyvSU0cFxcX5ObmGq9wNESS4Kaf8/f3R3p6eqPvnz17NmbMmGHGrSAish4SmHrEx6O0TjBqLi9vb+xJTLS6g1qZM0OC9/3336+ucMsoJLkyrvPx8an3GdmG5cuXGx9LrJCDf6nfJqOkampqjK/JsDNTMpTM9KAgIiKiFbaKyHH/bsXH90BJSalZ1uft7YXExD1W93fLmv/WW/Pfe7IfjN1E9hVvpYxxE6exOyVHiN1Etoix24qS6NOnT1c7XEqxPPnkk/WuYnp6eqqrEidLosvkpKaf0z/TmGnTpqmkvWkS3vRKBxGRLZOeJZJUufHRl9EupnOL1nUsJQlfvjhVrdPaDmhlFNOqVauwZs0aNelIU/6Oy/wZM2fOxLx58zBixAj83//9H6688kpVWkyucsvoJ4kJEuBfeumlWp9dv349Pv30UzUs7cUXX8Q111zTiltH5Fjkb4yc0H/x+PWIjwlr0boSU7Iwcda3Vvl3y1r/1lv733uyH4zdRPYTb//YuA9PfbQE79x9CYb07dqidTlK7CayRYzdVpJEl6sSc+bMUTvIzc1NzfIqk4SaKiwsrDcsoC75XFZWVpM/Iwl3cw05IiKyVpJUie7aC/ZKJqUODw9XM3fLfBoSR26++Wa89957jX5GAv6CBQvwwAMPYOrUqaoUmNRnExdeeCEuueQSNbeGBPfHH39czSKuk4mvZQ4OufIuZcnkIjARmZec0A/oFmXpZtgUe/9bT/aFsZvIfuKtJL5Fl/ZBjN1Edoyx2wqS6MnJyWpmV0mi67V0Bg4cqK5SmL5HrkpIkvxk5HNSm0dmbxUySalc2SAiIvv18ccfq2D+66+/qrk0duzYoWYAlyvZf/31V6Ofk+AvE6PU5ezsjPnz59d6TuKUTmKRfBcRERE1D2M3ERGRbWHsrs0Zbay0tFRdTbjiiitw1VVXqZlcZRk6dKjqzi8/IDFr1izV7V/qoou8vDxUV1fXW5907f/666+xc+dOtZ633noLl156aVtvFhERtaHhw4ejqqoKvXv3RmRkJG6//Xa88MILal4MIiIisj6M3URERLaFsdvCPdEXL16s6t/IUrfnuXTZlysQ0t1frk6YXtWQuujSy7x///611icF7WWIwFlnnaXqoXft2hV33313m24TERG1LZnte9myZW3yXc8880ybfA8REZE9Y+wmIiKyLYzdFk6iSw90md21IbGxsUhKSlKzsw4ePBghISHG1xr7jHj++edV4fq0tDQ1ZOBUddSJiIiIiIiIiIiIiKx6YtHGREREYPTo0af9OamtrtdXJyIiIiIiIiIiIiKyyZroRERERERERERERES2gkl0IiIiIiIiIiIiIqJGMIlORERERERERERERGQrNdGJiMixpKSkIDs7u82+LzQ0FDExMW32fURERPaGsZuIiMi2MHa3HJPoRERk0UDeIz4epSUlbfadXt7e2JOYeFoBfdmyZbj44ouRkZGBdu3aqefWr1+Pe+65B/v27cOgQYPw6aefIioqCs888wxmzJgBV1dXxMbGYsqUKfjvf/8LJyenVtwqIiKitsHYTUREZFsYu82DSXQiIrIYuRIugfzGR19Gu5jOrf59x1KS8OWLU9X3nk4wX7x4MTRNw5IlSzBx4kSUlJTgiiuuwAsvvIARI0bg/vvvx3333Ycff/xRvX/UqFGYO3euOgiQ5ysrK/Hkk0+24pYRERG1DcZuIiIi28LYbR5MohMRkcVJII/u2gvWSoL4BRdcYAzmiYmJyMvLw+TJk9XrTz/9tLoSrnNzc1NXx2+66SaUlZWpQP7444/D2ZlTkRARkX1g7CYiIrItjN0twyMCIiKik8jKysL27dvx8MMPq2AuOnTooALzzJkzUVVVhTPOOAO//PJLg5+/9NJL1ToOHTrUxi0nIiJyTIzdREREtiXLBmI3k+hEREQnsXTpUnTr1k0NHzt+/Dh27tyJ8PBwfP7553jllVfQpUsXdb8xkZGR6jYzM7MNW01EROS4GLuJiIhsy1IbiN1MohMREZ2iLtvgwYPh6emprnzrV8WvvfZaHD58GJMmTcLtt9+OqVOnNvh5fWITqe1GRERErY+xm4iIyLYstoHYzZroREREJyHBWyZE+emnn9TEJoGBgRg3bhxKS0vRuXNnVZNN6rZdeOGFajKTuo4dO6Zu9dnFiYiIqHUxdhMREdmWJTYQu9kTnYiIqBEJCQlIS0vDypUrsW3bNnzwwQf4+++/1TCyKVOmGN93/vnnw9XVVU160tAV9YiICMTFxbVx64mIiBwPYzcREZFtSbCR2M2e6EREZHHHUpKs8nskEEvttUGDBqnHISEhuPXWW9UQs7Vr1+Krr75Sgfz9999XNdh69Oih3ldZWWk8CHjkkUfULOL68DIiS0lJSVG9O8wlNDQUMTExZlsfEdkWxm4iIiLbwtjdMkyiExGRxUgSzsvbG1++2HBds9Yg3yff29QhZTJcTOfn54ezzz4b6enp+PjjjzF9+nR1X58l3N3dXb3vjz/+QGxsrDoQmD17Nu64445W2x6ipibQ4+N7oKSk1Gzr9Pb2QmLiHibSiRwMYzcREZFtYew2DybRiYjIYiT5ticx0ay9Y83Ze3bBggX1nluzZo3x/oQJE+q9LrXaZCGyJvJ/TBLoXzx+PeJjwlq8vsSULEyc9a1aL5PoRI6FsZuIiMi2MHabB5PoRERkURJYmYQjahuSQB/QLcrSzSAiG8fYTUREZFsYu1uOE4sSERERERERERERETWCSXQiIiIiIiIiIiIiokYwiU5ERERERERERERE1Agm0YmIiKhJZCKauLg4HDp0SD3+5JNP4OTkVG+R50+lb9++tT4zZcqUNtgCIiIiImqS0lwgMwFI/hs4vAZI3woUZwGaZumWERFZBCcWJSIioiYl0MeMGWNMoOuzpF955ZXGx0VFRTjjjDMwdOjQk66rpKQESUlJyMzMhJubm3rOw8OjFVtPRERERE1SkAYcXgvkJDX8ulcQujlHwI1dMonIwTCJTkRERKc0btw4lTTfsGGD8Tl3d3e16N59911cddVV6Ny580nXtXXrVtUTPSwsrFXbTERERERNJD3MD60CUtaeeMIJ8IsEfMMA6Xxeng/kpaoe6gNdcrHnXl/k1GRZuNFERG2HSXQiIrKolJQU1cu5rYSGhiImJqbNvs9ezJs3T5VyeeCBBxp8vaysDG+++WatJHtjNm7ciCNHjqgkemVlJcaPH4833nij0d7o5eXlatEVFBS0YEuIiKilGLuJ7ExNFbD7p397n7frDXQ8V/U6r6WqHMjcjdJ9K9EpqBydqjcBSS5A3PmAs4tFmk5ETcPY3XJMohMRkUUDeXx8D5SUlLbZd3p7eyExcU+TArrU9p48eXK95z/++GP1vLOzM6KiolQv7ZkzZ9bqlW1vJIF+MvPnz8egQYMQGxt7ynXt3bsX5513Hp555hnk5eXhxhtvxOuvv47HHnuswffPnj0bM2bMaHbbiYjIfBi7ieywB/qe3w0JdGdXoNtlhiR6Q1w9gPYD8EtCDY5v+wP3DHQHjmwAijOBXlcBLvz/RGSNGLvNg0l0IiKyGLkSLoH8i8evR3xM65f2SEzJwsRZ36rvbUow12t+79y5E+effz5yc3PV899//z169+6NpUuXYv369bj77ruRk5ODDz74AI7qvffeU0nxpr7X1NNPP4233nqr0ST6tGnT8NBDD9Xqid6hQ4cWtpiIiJqDsdv+/fLLL3jwwQdV0kX22VdffYX4+Hjs2rVLJTMOHDigJgR/6aWX1OTgZOOSVwJZewAnZ6D3tUDQqTtEVMMV9/5RhnMGD8IZ2i4gNxnY/hXQ53rAzatNmk1ETcfYbR5MohMRkcVJIB/QLQrWRq/57efnpx4HBgaqW1dXV7i4uKBdu3a44oor4OXlpSbdlJPJ4OBgOBo5mZbl4osvbtbnw8PDkZaW1ujrUuaFE48SEVkXxm77JBN/S6JcLngPGzYM9913n0qYL1++HGPHjsWll16Kr7/+Gvfff3+jPQfJhhxPAlLXG+53H9WkBLqpTOcIoHdvYOd3QGGG4bbfOPZIJ7JSjN0tw/mUiYiIWujCCy9Ut9u2bYMj+vbbb9XBjJubW5PeP2TIEKSmphofr1u3Dh07dmzFFhIREdXm6LG7MYmJiXjhhRdw/fXXq6TFXXfdpSYEX7hwIfLz8/Haa6+pCcRnzZqFDz/80NLNpZaQ+ub7FxnuR53VeAmXU/GPAvrfCLh6AoXpwO6fgZpqszaViMgaYjd7ohMREbWQXCGXiVMyMzPhiBYtWoRJkybVe17qnUtvAuk9YKpXr1644447MH36dOzZswevvvoq5syZ04YtJiIiR+fosbsxclG87jwmXbt2xfbt2zF48GB4e3ur5/v27YuEhIRG18NJwa1vIkC5QFLLwRVAeSHgGQjEDWvZyn3CgN7XATu+AnIPAknLgK6XtGydRERWFruZRCciIjIDqQmqycRMDqa0tBQbNmzA3Llz670WFBSkeq/179+/1vOvvPKKGv49fPhwVcrl5Zdfxs0339yGrSYiInLc2N1UFRUV6kK3zEsiZdtMJxmXfScXyaVurcT7ujgpuPVOBFhYVAQUHgUytv1bxsWlaaMJTyogCoi/Atj9A5C+BfCLBCL6tHy9RERWEruZRCciImqh6upq1UtIhj3bu7oHLFKXzrSn2cneq5Madz/99FOrtI+IiKgpHCl2N5eMGPPx8VE10Z988sl685N4enqipKSkwSQ6JwW3vokA/9i4D099tARlZWXAoR2GJ8N7AoGnnvSvyUK7Ah3PAw6vBvYtAnzDAV/+HyMi+4jdTKITERG10MqVK9XtgAEDLN0UIiIiagLG7pOTiUSl1Nr69evVnCcygduuXbtqvaewsFBNBNcQTgpufRMBJqZkqdvAmlwgJ0n6cwKxQ2F2Hc81TDIq35H4G3DmJMCZqScisv3Yzb9kRERkcfpBvS19j1wFP3bsGP755x/cfffdauItfRZxIiIie8fYbb+Sk5Mxfvx4lUTv2bOnem7gwIGYN29erffISDRJrpNt6Vqzz3Anoi/gVX8UQYs5OQHdRwObPgRKsoHklUDni8z/PUR02hi7W4ZJdCIishiZFMTb2wsTZ33bZt8p3yff21LSG6t9+/bo2LGjmiTzscceM0v7iIiIrBljt/3PdSKTi15xxRW46qqrUCT1swEMHTpUlWT5+OOP1bwms2bNwogRI+pNHk7W7az2zgjWcgAnZ6DjOa33Re7eQPeRwK7vgSP/ACHdgECW8yGyFMZu82ASnYiILCYmJgaJiXtUXbO2IoFcvvd0yMSYpvW9J02apBYiIiJHw9ht3xYvXoyEhAS11O15/sEHH6ge6lOnToWzszP++usvi7aVTt99Z58ovxMeD3gGtO6XhXQBIvoBR7cD+/8EzpwMOPOiC5ElMHabB5PoRERkURJYTze4EhERkeUwdtsv6YHe2MTgsbGxSEpKwubNmzF48GCEhIS0efuo+TxRjrG93QwP2p/VNl/a6QLg+H5DWZfUDa3b+52IToqxu+WczbAOIiIiIiIiIrJzERERGD16NBPoNqiLcyrcXZyQ5xQI+Ee2zZe6ef1bDz1lLVCa1zbfS0TUCphEJyIiIiIiIiKyV1oNujqnqruHnTu27XeH9wQCY4CaKsMko0RENspiSXSpwxMXF4dDhw6px5988gmcnJzqLfL8qfTt27fWZ6ZMmdIGW0BERKerseHBjoz7hIiIrBnjVH3cJ2Rzcg/B26kc2SU1OOYU0bbf7eT0b2/0rESgIK1tv5/IATFOtc4+cbZUAl1m/NYT6GLChAnIzc01LqmpqaoIvcwCfjIlJSWqLltmZqbxs2+//XYbbAURETWVm5ub8W821abvE30fERERWQPG7sYxdpPNObZL3Xy9qwqakwXSQL7tgIg+hvtJyyWb1fZtIHIAjN2tG7stMrHouHHjVNJ8w4YNxufc3d3Vonv33Xdx1VVXoXPnzidd19atW1VP9LCwsFZtMxERNZ+LiwsCAwPVBU/h7e2tRg45+pVwCeSyT2TfyD4iIiKyFozd9TF2k02qKgey96m7n22vwHPnWqgdsecDmXsMPdGPHwBCu1qoIUT2i7G7dWO3RZLo8+bNU6VcHnjggQZfLysrw5tvvlkryd6YjRs34siRIyqJXllZifHjx+ONN96Ah4dHg+8vLy9Xi66goKAFW0JERKczEZXQAzoZSCDX9w0REZE1YexuGGM32RRJoNdUoUDzxj/pFsx/ePgBUWcCqeuBQ6uAkC6GUi9EZFaM3a0Xuy2SRJcE+snMnz8fgwYNQmxs7CnXtXfvXpx33nl45plnkJeXhxtvvBGvv/46HnvssQbfP3v2bMyYMaPZbSciouaRK+CRkZEIDw9XFz3JMJSMvdiIiMhaMXbXx9hNtlrKJbkmCsBRy7alwyAgfQtQnGlI7od1t2x7iOwQY3frxW6LJNFP5b333lNJ8aa+19TTTz+Nt956q9Ek+rRp0/DQQw/V6oneoUOHFraYiIiaSoIXTz6JiIhsB2M3kY2qKAHyUtTdQzWRlm4N4OYFRJ0FpKwFDq8GQrtZukVEdoux2/ysLol+4MABtVx88cXN+rxcaUlLa3y2Zynz0lipFyIiIiIiIiIiu3B8v1QEVhN7FuV6wypEnw2kbQaKswy10WEl7SIiOgULTMt8ct9++y3GjBnT5NlShwwZgtTUVOPjdevWoWPHjq3YQiIiIiIiIiIiK3diQlGr6vHt5gm0P8NwP/XU8+AREVkLq+uJvmjRIkyaNKne81Lv3M/Pr95QhF69euGOO+7A9OnTsWfPHrz66quYM2dOG7aYiIiIiIiIiMiKVJUDuYf+TaLvb3zEfnPtT89B2L7TX69rdRR6wxnOBUeQnXMi0U8WlZKSguzsbLOsKzQ0FDExMWZZF5E1saokemlpKTZs2IC5c+fWey0oKAhbt25F//79az3/yiuvYPLkyRg+fLgq5fLyyy/j5ptvbsNWExERERERERFZkZyDgFYNeAUB3qEAzJdEz84vVrf3vbsEgCynb+5YT9w2wB1lSavV44yMDLO1j04/gd4jPh6lJSVmWZ+Xtzf2JCYykU52x6JJdE3Taj328vJCeXl5k96rCwwMxE8//dQq7SMiIiIiIiIistlSLiHdACcns666sLRC3Y648jr07RXfrHUcRw5qtM9weXc3dA0uV9UHyDKkB7ok0G989GW0i+nconUdS0nCly9OVetkEp3sjVX1RCciIiIiIiIiohbQaoDcg4b7oV1b7WuCQ8MQ3ew56TriUPYmdCpPwD1nN21OPGpdkkCP7trL0s0gslpWN7EoERERERERERE1U0G6oSa6qyfg3x7WapvvUHU7ub87XGvKLN0cIqKTYhKdiIiIiIiIiMie6qGLoDjAyXrTPoc9uiGjOgD+Hk7oVLDB0s0hIjop6/1rSkREREREREREpyc32XAbHAer5uSMpWU91d1ueX/JZHiWbhERUaOYRCciIiIiIiIisgcVJUBhhuF+UCdYu9XlXVFUoSGg8hiQss7SzSEiahQnFiUiIiKyUikpKcjOzm7xehITE83SHiIiIrKRXug+4YCHL6xdmeaOr3dVYsoAd2Dzp0DHcyzdJCKiBjGJTkRERGSlCfT4+B4oKSk12zoLi4rMti4iIiKyQrkn6qEHW38vdN28LSeS6Ak/AyNfALyCLN0kIqJ6mEQnIiIiskLSA10S6F88fj3iY8JatK4/Nu7DUx8tQVlZmdnaR0RERFZGaornHjbcD4qFrdiYVo1c9/YIqkgHdnwHDLrd0k0iIqqHSXQiIiIiKyYJ9AHdolq0jsSULLO1h4iIiKxUaQ5QUQQ4uQAB0bAlBwLOxcCs74AtnzGJTkRWiROLEhERERERERHZurwTvdD9owBn2+ozechvEODiDhzbCRzdaenmEBHVwyQ6EREREREREZGty0sx3AbGwNZUuHgD3UcaHmz/2tLNISKqh0l0IiIiIiIiIiJbr4duTKJ3hE3qO85wu/M7oLrK0q0hIqqFSXQiIiIiIiIiIltWkg1UlhjKuPhHwiZ1GQF4hwBFx4CDf1m6NUREtTCJTkRERERERERkF/XQo22uHrqRqzvQ+1rD/e3zLd0aIqJamEQnIiIiIiIiIrJlNlwPvZZ+Nxhu9y4EKkos3RoiIiMm0YmIiIiIiIiIbLkeev4R+0iitx9gqOkupWn2L7Z0a4iIjJhEJyIiIiIiIiKyVaW5hqSzkwvgFwGb5uQE9LrKcH/3T5ZuDRGREZPoRERERERERES2Kj/VcOsXabv10E3pSfR9fwIVxZZuDRGRwiQ6EREREREREZGtKjhRyiUgGnYhsh8QFAtUlRoS6UREVoBJdCIiIiIiIiIiW5WfZl9JdJZ0ISIrxCQ6EREREREREZEtknInpTmG+/52kkQXehJ9/xKgvMjSrSEiYhKdiIiIiIiIiMimS7n4hAFunrAbEX2B4E6Gki77WdKFiCzPDmacICKyjJSUFGRnZ5tlXeXl5fDw8GjxehITE83SHiIiIiIisgH5R+yvF7pe0qXnlcDq1wwlXXpfY+kWEZGDYxKdiKiZCfT4+B4oKSk12zGipsFsioo45JGIWle0vxOCU/8Esn4AcpKBsjyguhJw8wJ82wEhnYH2ZwAxgwHPAEs3l4iIyD4VpBtuA6Jgd6SkiyTR9ZIuHr6WbhEROTAm0YmImkF6oEsC/YvHr0d8TFiL1vXHxn146qMleOfuSzCkb1ezrKusrKxF6yFq7Pd+4MCBWLFiBWJjY9Vz999/P95++23jezp37owDBw6ccl0rV67EnXfeiaysLDz++ON46KGHWrXtZCaVpcCxXeiRtQWpD/oBW1849Wec3YC4oUCf6w0nw/Y01JyIiMiSaqqBomOG+37tYXci+gDBnYGcJGDfIqDPtZZuERE5MCbRiYhaQBLoA7q1rNdHYkqWuu3SPshs6yJqjQT6mDFjcOjQoVrPb9q0CQsWLMA555yjHru4uJxyXZI4v/zyy/Hwww9j/PjxGDduHM444wwMHz681dpPZkiep24A0jYDNZXwBlBdo6EsOB4+3Ycbapb6hAIu7oYJzgozgMxE4Mg/hhPfpOWGZfETwKA7gcF3AR5+lt4qIiIi21acBdRUAa4egFcQ7I4M15UL8KteMZR0YRKdiCyISXQiIiI6JUl0T5gwARs2bDA+V1VVhd27d+P888+Hr2/Th9d++eWXaN++PZ566ik4OTnh6aefxocffthoEl3mDJBFV1BQ0MKtoSaTOlPHdhkS4DKxl/AJR4pLLPo/sRRL1/wPAwYMOPk6svcDu38GNn9imPxsxfPAhveBC58ABkyCteP8F0REZLXkorXwizQknO1R/FhDEl2ORSrLOKKNiCyGSXQiIiI6pXnz5iEuLg4PPPCA8bmdO3eipqYG/fv3R1paGoYNG4a5c+ciJibmpOvavn27SphLAl2cffbZeOyxxxp9/+zZszFjxgwzbg01SWUJsOd3IOeg4bFPGBB7PhDSBdn705FbtrRp6wntCgybCpz3IJDwM7BilqF3+u8PAtu+gmfXu+Eo818A8jtvvgkwOP8FEZGD0+uh22MpF11kP8A/CihIA5L/BrpdYukWEZGDYhKdiIiITkkS6HUlJCSge/fuqiZ6aGgoHnzwQdx+++1YtGjRSdclPcl79uxpfOzv74/09BMngQ2YNm1arZrp8vkOHTo0e1uoiSflCT8B5YWAsyvQ8TwgeiDgfOpyPY1ycTUMw+55JfDPPGD5TODIRvTI2I5bz3Az7+zKVjz/xeg7nkD3vme2aF2JG1di4advcv4LIiJHZ9oT3V5Jp4vuI4F/PgD2/sEkOhFZDJPoRERE1Cw33nijWnTvvvuuSrZLklsS441xdXWtVdLC09MTJSUljb5f3muOEhjURFJ+JfEXQ41Vqa/a8yrAN9x865dkutREj78c+OUeOB9cgQ8u98Lx/LVAzdWGpL0dz38R0r4jorv2atG6jqUktejzRERkB6rKgZIT5cb87bgnutCT6DK5aE0N4Oxs6RYRkQPiXx4iIiIyi/DwcFXeJSPjRK+oRgQHB6vJRXWFhYVwd3dvgxbSKR3dAez+0ZBAl8lCpWa5ORPopgKigIk/4kjPO1BVoyGk9CCw7UugnCVKiIiImtwL3cMfcPeBXYsdCrj7GrY5Y5ulW0NEDopJdCIiImqWqVOnYv78+cbH69atg7Oz8ylLrQwcOFC9V7d161ZERbWsly+ZwdGdhmHSUrM7oi/Q+1rAtZVHADg7I7PLOFzyeQmqnNwNJ8fbPgdKc1v3e4mIiOwliW7vvdCFHI90uchwf+9CS7eGiBwUk+hERETULP369cOTTz6JZcuWYfHixbjzzjtx0003wdvbW70uZV0qKyvrfe7yyy/HmjVrsHTpUvX6Sy+9hEsvvdQCW0BGWXtOJNABtB8AdBsJOLXdYeKKQ9XYGzoS8AwEyvKBrZ8DRZlt9v1EREQ2xxHqoZvqPspwqx+vEBG1MesrOklEREQ2YeLEidi9ezeuueYauLi4qMezZs0yvt63b1+88cYbuPLKK2t9TiYhff311zFq1Cj4+voiMDAQn3zyiQW2gJT8I0Dib4Ye6JH9gC4XGybxamPlrv7AGROBnd8aEujbvwL6jW+9cjJERES2Pgm48LOPnujJycnYsmVLo6+7VLRDXzjD6dgu7Fr9Byq8Ixp9rxxrxsTEtFJLichRMYlORERETaZpWq3Hs2fPVktDDh061Oh6pNe69D7fs2cPhg4dqpLpZAFSNmXXD4BWDYR0BbpeapEEupHUO+03AdjxNVB4FNghifQJgE+Y5dpERERkbcoLgQqZQ8QJ8GsHW1ZSXqVun3rqKbWczF83e2NYrCvef/hKvLOx/mhHnbe3FxIT9zCRTkRmxSQ6ERERWURcXJxayEKqKwyTiFaVGoaCx49t0xIujXL1BPqMA3bqifRvDT3UPQMs3TIiIiLrUHiiF7pcZHax7cnZyysNSfRHrxuM6y8686TvDS9KAAo34/kr4jD5losbfE9iShYmzvoW2dnZTKITkVkxiU5ERETkaGREwb4/geIswM0H6HWNdZ2Eu0ki/QZg2xdAyXFgxzfAGf8B3Lws3TIiIiLLK9AnFbWfeujRYX4Y0O0UE82XeAH/bIZ/ZSYGdAoxXHgnImojVtDdiIiIiIja1NEdQOZuwzDwnlcAHlZYTkcS5n1vADz8gdIcIOEnoKba0q0iIiKynp7odlIPvcm8gwHvEECrAXKSLd0aInIwTKITEREROVod9ANLDffjhgGBVjzUWRLofa4z9JLPSwGSllm6RURERJYlCWQpdyakHJujCeliuD1+wNItISIHY7EkutSnkjqoppOO3X///XBycjIuXbqc+ON4CitXrkR8fLyagfm1115rxVYTERER2S4nOfFO/A2oqQQCYoAOg2D1pN5rj7GG++lbgPStlm4RERGR5UiZM5nXxNkN8AmFwwnubLjNPWi4oEBEZM9JdEmgjxkzplYCXWzatAkLFixAbm6uWrZuPfVJUlZWFi6//HKMHz8e69atw5dffokVK1a0YuuJiIiIbFNsTbJhCLiLB9BjNODkBJsQ2hWIPd9w/8ASQ690IiIiR1R4oh66X4R1TAje1vyjDMcxlaX/9sgnImoDFvmLO27cOEyYMKHWc1VVVdi9ezfOP/98BAYGqsXPz++U65Kkefv27fHUU0+ha9euePrpp/Hhhx+2YuuJiIiIbE+nICd0rjkx9LnLRYBnAGxKzBAgLN7Q62z3T0BZgaVbRERE1PYcuZSLcHYBgmIN93OSLN0aInIgFkmiz5s3T5VuMbVz507U1NSgf//+8PLywmWXXYaUlFP3Mtq+fTuGDx+uyr+Is88+G5s3b270/eXl5SgoKKi1EBEREdk3De+P8YILaoDAWKBdH9gcOdbrPgrwbQdUlQJ7fuUwbiIicuAkegQcVnAnw23OQUu3hIgciEWS6FILva6EhAR0794dn3/+OXbs2AFXV1fcfvvtp1yXJMFN1+fv74/09BMzVTdg9uzZCAgIMC4dOnRowZYQERERWb9YpwyM6OSKajn063ap7ZRxqcvFDeh5pWGi0fwjwOG1lm4RERFR25GLx8WZhvu+TKKr0jYVJZZuDRE5CKspoHXjjTeqmuhDhgxRZVneffddLFmy5JQ9xSXZ7uHhYXzs6emJkpLG/4hOmzYN+fn5xiU1NdWs20FERERkVaorcIbLXnU3ybkL4BUEmybt73qJ4f7hNUA+j+WIiMiBJhWtqTJcTLb1eN4SHn6AT/i/E4wSETlSEr2u8PBwVd4lI+PEpBmNCA4OVpOL6goLC+Hu7t7o+yXhLr3VTRciIiIiu3V4HbydypGUU4PDzidqiNq6dr2Bdr1UmRok/gZUllm6RURERK2v6JjhVhLItjqqzFxY0oWIHDWJPnXqVMyfP9/4eN26dXB2dj5luZWBAweq9+q2bt2KqKioVm0rERERkU0ozQOObFR3H1pchhonF9iNLpcYeuGVFwD7/gA0zdItIiIial2FJ5Lofu0s3RLLC+5suM1J5hwpRORYSfR+/frhySefxLJly7B48WLceeeduOmmm+Dt7a1el7IulZWV9T53+eWXY82aNVi6dKl6/aWXXsKll15qgS0gIiIisjKH/ga0amTUhODXvVWwK64eQPwVgJMzkL0PyEywdIuIiIhaV9GJSUUduR66LiAKcPEwTDauT7ZKROQISfSJEyfihhtuwDXXXIPx48fjsssuwzvvvGN8vW/fvliwYEG9z4WGhuL111/HqFGj0K5dO+zdu1cl44mIiIgcmpxQnkgsb63uDrvkFwF0PNdw/8ASuFaXWrpFRERErUNGXBXpk4qyJ7q6iB50okxdTpKlW0NEDsDVkl+u1Rl2O3v2bLU05NChQ42uR3qtS+/zPXv2YOjQofD19TV7W4mIiIhsSvJKw214L+Sm2fEcMB0GA9l7VWKhQ8E/lm4NERFR6yjLA6rLASnN5h1i6dZYT110OQaQuuixQy3dGiKyc1bTE72l4uLiMHLkSCbQiYiIiPIOA7nJhl5a9n5S6ewCdBslXdIQVHYYV8dbtI8IERFR604q6htuiH307+SihRlARYmlW0NEds5ukuhEREREdMKh1YbbyP6AVyDsnpR1iRmi7r47yhMuFfmWbhEREVHrTCrKUi7/8vAzXFQQuQct3RoisnNMohMRERHZWy/0/FTDcO8TiWWH0PEclLoGoJ2vM6J3zYE9kRKIx4vKkZhRgI3JOUhGOALOm4C0Sm+k5ZWisrrG0k0kIqI2m1SUSfRagjobbnOSLd0SIrJzHO9KREREZJe90PsZemg5CmdXHA4Ygq5ZCxFyZAlwcCXQaRhsWW5xBXak5WN/ZiGKy6tNXglB4LkTcKASOLD5CFycnRAb4o2ekf6IC/WBk5OTBVtNREStM6mo3hM9wtKtsS7BcUDqOkNP9Drz7hERmRN7ohMRERHZi7xUk17og+FoStzD8N7mSsODP6YCVRWwRXklFViwIwOfrT+Mbal5KoEuifKoQC/ER/qhPY6jcMvvCHYug4+HC6prNCRlFeO3HRmYvzEFB7OKLL0JRERkThVFQKXU/HYCfMMs3Rrr4h8FuLgDlaX/XmggImoF7IlOREREZC9S1xtuI/oAHv5wRE8uL8Pt54bDLXsvsP5d4Lz/g62QZPjGQznYdCgHNZpKlSA21Ad9ogLQIcgLri6G/i+bMzZi3ZL3MOa8Aeg3uDeyiyqw52gBdqUVqPuSTO8S5osLuofBx4OH+0RENq/wRCkXn1A18opMyCSrgR2B4/uBHKmL3tHSLSIiO8We6ERERET2oCgTyEky9FLrMAiOKq8MSOt5h+HByheB/COwld7n321OVTXPJYHeMcQbEwbF4PJ+7VWJFj2BXpeUbgnz88DQrmGYfG4szuwYBKnmciCrCF9uSMGRXOm5SERENs1YyoX10BsU3Mlwq5LoREStg0l0IiIiInuQusFwG9Yd8AqCI8vpcAnQYbBh6Pufj8PapeaU4Jt/UnGsoBwers64rFcEruwfhVBfj9Naj6ebC87rEorxA2MQ6uuO0spq/Lg1DdtT81qt7URE1JaTirIeeqN10UVBGpxrbLOUGxFZPybRiYiIiGxdWQGQmWC4L8ljR+fkDIx+1VAbPuEX4MAyWKvEjAL8vC0NZVU1aOfvgRsHxaB7RMsmhJWe6def1QE9IvzUHGt/7cvCuqTj0DjhGhGR7Y42E+yJ3jDPQMArWGZghX/5iQsORERmxiQ6ERERka3L2KpOHBEQA/ixl5oS0Rs4+3bD/YX/BapPTDhqRXYeycfihGOqfEu3dr64dkA0/DzdzLJuNxdnXNKzHYZ0DlGPpdb63/uzmUgnIrIxrtVlQHmB4YFvuKWbY/UlXfzL0yzdEiKyU0yiExEREdmymiogY5vhftSZlm6NdRk+DfAOBY4fADZ9BGuy40gelu819CzsFx2gSrg0Vve8uaRe+tmxwWqCUbEtNQ/rD+aY9TuIiKh1eVWd+LstPa1dT6/Ml0MJMpR08avIsHRLiMhOMYlOREREZMsyE4HKUsDDDwjtaunWWBfPAODCJwz3/5oNlObCGuw9WogVe7PUfZkIdFi3MJXwbi39ogNxQbcwY4/0rSnWsR+IiOjUvCtPJNFZyuXkAmNUGTeP6mL0CGWqi4jMz7UV1kkOICUlBdnZ2WZbX2hoKGJiYsy2PqK2kphiSIK0RPJRJjOIqAXStxhuI88w1AKn2s64CdgwF8hKBFa+DFw2y6LNya12x6oEQ73WvlEBOLdzSKsm0HX9OgSivLpG1UaXsi4BXm7oFObb6t9LREQtwyR6E7m4AYEdgNxDuKwLU11EZH78y0LNSqDHx/dASUmp2dbp7e2FxMQ9TKSTzcjIMAwTnDjrW7OtMzOv2GzrIiIHUZAOFGYYJtCM7G/p1lgnF1fg0pnAF9cAG+cCA28FQjpbpCmuwdHYXR6MGhhqoEuZlbZIoOsGdgxCYVkldqUVYNHuo7juzA5qElIiIrJeXnoSnXOenFpQJ5VEv7QzU11EZH78y0KnTXqgSwL9i8evR3yMYWhwS3vySiJS1sskOtmKvLw8dTv6+ono3tUwiU1zbdqRiL9/+w75JeVmah0ROYy0zYbb8HjA3dvSrbFeXUYYlgNLgSVPA+O+bPMmlGjuCL/2aVTDGZEBnri4Z7s2TaAL+b4LuoUjv6QSqbmlWLAzA+MHdmjTNhARUdP5uQOe1YWGB+yJ3rTJRQ8ux7COLthTzXMrIjIvJtGp2SSBPqBblKWbQWRRIeHhiO7YsUXr2J9mvtJIRORAKoqBrD2G+5xQ9NQueR5IWgHs+R04tBqIPa/NvrpGA76rOQ9uQe3h4VSFMX0j4epsmdI7Ls5OGNUnEl9tTEF+aSWWJB5DP0+LNIWIiE6hf4SL4Y6HP+DmZenmWD/vEFQ4e8PLrQR+x7cDGGLpFhGRHWHhTCIiIiJblLEd0KoBv/aAX6SlW2P9wnsAZ04y3F/8JKBpbfbV7x4Mxz5Eo6ayHL09cuDtbtl+LJ5uLiqR7uLkhKSsYhwo5CkBEdUmo4Tj4uJw6NAh43P333+/GtGiL126dLFoGx3BgMgTSXT2Qm8aJycUeLRXd/0y/7F0a4jIzvCImYiIiMjWaDVAxlbD/agBlm6N7bhgGuDmA6RvBRJ+aZOvXJ/jg1cPGJIfOUv+B1/nKliDdv6eGNo1VN3flecCt9CWjaoiIvtKoI8ZM6ZWAl1s2rQJCxYsQG5urlq2bj0Rh6jVDIg8kbJhPfQm05PoAZkbLd0UIrIzTKITERER2ZqcZKC8EHD1AsJ6WLo1tsM3DDjnXsP95c8B1a2b0M6rcMGDOztAgxMGOB1A8c6lsCZ9owMQF+qDGjghdOwjqKhuu975RGS9xo0bhwkTJtR6rqqqCrt378b555+PwMBAtfj5+VmsjY7iDL2cC3uiN1mhRySqajR4FqUAeSmWbg4R2RHWRCciIiKyNcd2Gm7b9QSceTh3WobcC/zzAXD8ALDtS+DMm1vla6RazLSEKGSUuSPOuxxjyjbiJ1gXKccwIj4cn605CITH4etdhRg80NKtIiJLmzdvnirl8sADDxif27lzJ2pqatC/f3+kpaVh2LBhmDt3LmJiYhpcR3l5uVp0BQUFbdJ2e+JUVYaeYSf6PTKJ3mTVzu5Yf6Qa58W4AgeWAWdNbtH6UlJS1OgMcwgNDW30/wwRWT+edRERERHZkspSIHu/4X5EX0u3xvZ4+gNDHwH+nAb89QLQ9/pWmaztl4xALDwWCDenGrzVNwU7/rGOMi51SX32AcFVWJfthl/3FWNKWj56RwVYullEZEGSQK8rISEB3bt3x9tvv60SgQ8++CBuv/12LFq0qMF1zJ49GzNmzGiD1tovr8KDajLoSmdPuLn7Wro5NmXRgaoTSfSlLUqiSwK9R3w8SktKzNIuL29v7ElMZCKdyEYxiU5ERERkSzITDBOK+oazZ1pznXULsP5dID8V2DgXOPff3pbmkF3ughl7DDVZ7++ciT4BpdgB69XeW0Nx4t/wiT8fj/24Az/ffS5cXVj1kYj+deONN6pF9+6776pku/Qw9/f3r/f+adOm4aGHHjI+lvd16NChzdprD7zzD6jbErdgBDg5Wbo5NuXPpCrMvBBA8t9AdSXg4tas9UgPdEmg3/joy2gX07lFbTqWkoQvX5yq1skkOpFtYhKdiIiIyJYc1Uu5sBd6s7l5AsMfB36+C1j1GjDgZsAr0Gyrf2ZPFHIrXRHvV4o74zJhC3KWzUV432HYlVaAj9ccwm3nd7J0k4jIioWHh6vyLhkZGQ0m0T08PNRCzeedv0/dlroGg+ODTs/m9BpUugfArTwfOPIP0PGcFq1PEujRXXuZrX1EZJvYxYSIiIjIVhRlAkVHASdnQz10ar6+NxgmZS3LA9a+ZbbVLs70x+9HA+HipOHl3kfgZiNH2zXFebi5nyER9tqSfUjNMc/QdSKyD1OnTsX8+fONj9etWwdnZ2f2Lm9FXiY90en0yDTZhWFnGh5ISRciIjOwkcN6IiIiIjJOKBrSBXDztnRrbJuzC3DhU4b7G94Hio+3eJX5lc54MiFK3b89Ngu9/UthSy6K88LgTsEorazG4z/thCazoxIRAejXrx+efPJJLFu2DIsXL8add96Jm266Cd7ejEWtoroSXgUH1V0m0ZunIPxswx2ZXJSIyAyYRCciIqImkRqOUv/00KFDxud++eUXdOrUCa6urujfvz8SExObtK7LL78cTk5OxmXEiBGt2HI7UVMNHNttuM8JRc2jx2jDvqwoMktv9Of3tkdmuRs6+ZThgc7HYGvk/+Lsq/vC3dUZq/Zn49ft6ZZuEhFZiYkTJ+KGG27ANddcg/Hjx+Oyyy7DO++8Y+lm2a/sfXCuqUR+mYYKF04q2hwFYQMNdzK2AUVZlm4OEdkBJtGJiIioSQn0MWPG1EqgJyUlYfLkyXjhhReQlpaGbt26YcqUKU1a36ZNm7Bz507k5uaqRZLxdAo5SUBlCeDmAwSzXrVZyERtUhtdyASjLTjJ3pjrjW/TguEEDS/1OgJPF9vsxR0X6oP7L+yi7s/+Yw9KKqos3SQishAZjRIbG2t8PHv2bOTl5eH48eN488034ePjY9H22bUMw3TU245WG2IVnbYqz2Agoo/hQdJySzeHiOwAk+hERER0SuPGjcOECRNqPSe9ziWBfv3116Ndu3a46667sHXr1lOuSxLucmLeu3dvBAYGqoUn4qczoWgvQ010Mo9ulwHtzzBcoFj7ZrNWUa0B0xMNZVzGRefgrCDbric+ZWgndAj2wtGCMrz3V5Klm0NE5HgytqubrZJEp+brcmKkYxJLuhBRy/EMjIiIiE5p3rx5uP/++2s9Jz3Tb7/9duPjvXv3omvXrqdc18aNG1FdXY3o6GiVPJcEvfRGb0x5eTkKCgpqLQ6nsszQE13ovarIPKSH3wV6b/QPgMLTL8MyPzUYiYVeCHCtwtSuR2HrPN1c8PjIeHX//b8P4kiubV8UICKyOUcNPdG3ZNRYuiX2kUSXuug13JdE1DJMohMREdEpSS30k6moqMCrr76qJho7lT179qgJyhYsWID169cjOTkZ06ZNa/T9Mnw8ICDAuHTo0AEOJ3svoNUAPmGGhcyr68VA1FlAVSmw5vR6o+dWuODVAxHq/sNdjyHY3T56DV7WO0JNMlpeVYPZC/dYujlERI5Dkr0nRp+xJ3oLRZ8NuPsCJdnAUUPvfiKi5mISnYiIiFps+vTpqld5U2qiS8J8yZIlKpHep08fvPzyy/j+++9P+v78/HzjkpqaCoeTdSKJGWboHUytURv9xIWcTR8ChU3vTf7KgQjkVbqih28pJkQfh72QSUafHtMLzk7Agh0Z2JicY+kmERE5hrxDQHkBapzdsCebvadbxNUdiBv2b290IqIWYBKdiIiIWmT58uWYM2cO5s+fDzc3t9P+fHh4uJqkTMq2NMTDwwP+/v61FkfiWl0K5J6Y0DWcSfRW0/kioMMgoKoMWP16kz6yq8BTlXIRM+LT4WpnR9Y92/vjhoEx6v6M33ajusY2J0slIrLFeuil/p1QxRx6y3W50HDLJDoRtZCdHeoTERFRW5JSLOPHj1dJ9J49ezbpMzfccANWr15tfLxu3To1Makky6m+wLIUABrgFwF4BVm6OXZeG13vjf4xUJB+0rdrGjAjMQoanDA2Ig+Dgothjx65pBv8PF2xO70AP2w5YunmEBHZvwxDPfTSgC6Wbon9XCQXqRuAsnxLt4aIbBiT6ERERNQspaWlanLRK664AldddRWKiorUokl2EVATgFZWVtb7nJRwefDBB1Ui/eeff1blWu666y4LbIFtCCo7bLjDUi6tr9MFQMw5QHU5sOq1k771z0x//JPnAy+XGjzePQP2KsTXA/cONyRy3liyD2WVrM9LRNQWk4qWBHSzdEvsQ3AcENIF0KqB5L8t3RoismFMohMREVGzLF68GAkJCZg3bx78/PyMy+HDhqRv37591eShdT366KPqtcsuu0wlz++++2488cQTFtgC6xfp6wTfimOGB0yit21t9C2fwq3kxL6vo7IGeGlfpLo/pWMWIj3rXyyyJzefE4vIAE+k55fh83UnLuoQEZH5SUeEE+VcStgT3fy90Q8stXRLiMiGMYlORERETSa9zGNjY9V96YEuj+su+uuHDh3ClVdeWW8dUjf9ww8/VL3WMzIy8PTTT8PV1bXNt8UWXNfLDU5yxz8a8HSsWvAWE3c+EDsUqK5AxIGvGnzLN2nBOFjigWC3KtwelwV75+nmggdHGHpEzvnrAArK7PuiARGRxcjE1sVZgJMzSv06Wbo19qPLiH/rop8YMUlEdLqYRCciIiKyUuN6nbi4EN7D0k1xLMMeVTchKX+gvZ+6jGFUXOWMNw60U/fv73wMfq6OMevb1QOi0CXcF3kllXh/ZZKlm0NEZNelXBDaDZqrp6VbYz9izwVcPID8VCB7v6VbQ0Q2ikl0IiIiIivkXnIUQzq4qokrEcYkepuKPU/VRneuqcR/z3Wv9dIHh0KRXeGGjl7lmNAhB47C1cUZUy/tru5/uDoZmQVllm4SEZHdTiqKiL6Wbol9cfcBOg4x3GdJFyKytSR6dnY24uLi1FBv3S+//IJOnTqpId39+/dHYmJik9Z1+eWXw8nJybiMGHFiqA4RERGRjQpKW6Fui9zDAXdfSzfH8WqjX2DojX77AHe4Vpeo+1nlrnj/UJi6P7XbUbg7O9aQ8Et6tsOAmECUVdbgzWXsyUdEZHZHDfXQEdnP0i2x35IuScss3RIislGulkqgjxkzplYCPSkpCZMnT8Z7772HYcOG4b777sOUKVOwZs2aU65v06ZN2LlzJ6Kjo421VomIiIhsWWD6X+o21zMWfrB/iSlZZltHUztinJTmhw6enRCGg3DL2IQtLt6Ym90TJdUu6OKeh8j8BGwpaPrqko/mwtZJZ5XHRsbj+vfX4et/UjHl/9m7D+g4yqtv4P/tfaVV712WZNmSe6/YprmA6cUESAg98CZ5CSG8wEcSICSUAAkhdAi9hI4xrrhbli13FVu9911t79+ZGcnY2JZWZfv9nbNnHssz60e2vDNz5z73zs9CZozC39MihJDQy0RPLAKC/7QRgM1F/w+o2w7YzYBI5u8ZEUKCjF+C6Ndccw2uu+467Nmz5+TXmJudv/zlL7jqqqvYX99xxx1Yvnz5kO/V3NzMNjGbMGGCV+dMCCGEEOIzuiYodFVwud3QSlORhtDFNJdlrHn8ozF7zzVr1ozJ+5yfLcC6NQqku+qQ9f86IFnzEngCYPtbT2Jaw+ERvafJxGW1B6sZmVFYnBeLzZWdeGHjcTxz9SR/T4kQQkKDuRfQ1nPjhIlAb62/ZxRa4goAVRKgbwHqd/yYmU4IIYEcRH/llVfYUi733nvvya8xmemnqqysRG5u7pDvVVJSAqfTyWah9/b2YuXKlfjXv/4FjUZz1v2tViv7GtDXN4wUIkIIIYQQX6j4ht3saHBCkRTamVJarZbdLr9qDfJys0b1XqWHyrH1q49x/5WzcNWSqaOe26vf7sWepqOYmSLA+Tffi60uEeJ4Olx+w2oAzMtzJSV7sX39WlhtNgS7Xy8bxwbRPz/QjLvOy0F2LJUbIoSQUWvrfzgbmQbImHgGBdHHvFRbzhKg7D/AiU0URCeEBEcQnQmgD8Zms+Hpp5/Gb37zmyHfq6KiAsXFxXjqqafA5/PZEjAPPPAAWxbmbJ544gk8+uijI547IYQQQojXVXzNbj6rcGDNLISF6Lg4pKSnj+o9jjd3sduUWBWmjEse9ZySSqrwx61WvHhNBna4CtmvLUxyIkk6/HlWHq9GqChKicTSgjhsKO9gs9H/fs1kf0+JEEKCHzUV9b6TQXSmuejj/p4NISTI+K2x6GAeeeQRKBQKNiA+FCZgvn79ejaQPnHiRPztb3/DJ598Muj+Op3u5KuxsXGMZ08IIYQQMgqmHqCO6wnzRaXd37MJe98ed+Bx2zVwQoBJoiYkSenfhPE/S8ex2y8OtuBEh97f0yGEkODXNlAPncpkeU3WIoDHB7oqAS3FggghQR5E37RpE/75z3/ivffeG1GD0Li4OHR3d59WsuVUEokEarX6tBchhBBCSMCoWge4nTCrslDT6/b3bMKeUJOE790z2PGD/NchcRn9PaWAMCE5AuePj4fbDTy38YS/p0MIIcGv9eCPTUWJdzBlclKmc+Pqjf6eDSEkyARUEL22thbXXnstG0QfP368R8dcffXV2L59+8lf79q1C/Hx8WywnBBCCCEkWEu5aBPn+XsmBEDE3Gvh5vExT3AM03kVmGLY6u8pBVw2+teHWlDVTtnohBAyYjYT0FXFjamci3dlL+G2bEmXkbPYnWjoMaGsoRfbT3RhY3k7Nla044eqTuxv6EV1pwFGq2Ns5kwICd+a6GdjNpvZ5qKXXHIJVq9eDYPBwH6dKevC4/HYBqAymeyM7HSmhMuvf/1rPPvss+jq6mLLtdxxxx1++i4IIYQQQkZ5E31i4ylB9H/4e0ZhrVeggWL8QnY8R9MHGIDJhq3Yr1wEKz+0G756YnySGhdNSMDaI214buNx/PO6Kf6eEiGEBKeOY4DbBShiAVWCv2cT2piGolseB2p+AJx2QOB5BYRekw2VbXrUdhnRoT979YOf0shFyIlTIsLGG8WkCSGBIGAy0b///nscO3YMr7zyClQq1clXfX09+/tFRUX45ptvzjju/vvvZ3/vwgsvZIPnd955Jx588EE/fAeEEEIIIaNUsxlwmIGINJjVOf6eTdgrk88Ej8eHxtYGqzodXcJESNwWTDb84O+pBYx7l+ay228OtaKirc/f0yGEkCAv5VIM8CjY6lVJk7iyLtY+oKnUo0NaTDx8XNqIt3fVY09tz8kAeoRMhOxYBSalRmJWVhRmZUZharoG4+KViFaK2X16TXbsrevFhjYR4q/7C/Y0W+ByUbk+QoKRXzPR3UwRxX5MBvqpv/6purq6s36dyUx/7bXX2BchhBBCSFCr6E8YyF9ON9F+VmsUo1bCBYiTTccB3jjsUS3D8t63+7PRF8JG2ejIT1Bj+cREfHO4Fc9tOI5/rZnq7ykRQkjwNhWlUi7exxcA2ecBRz7lSrqkzz7rbk6XGzsbzUi8+QXs6mKy1S1grszSouXIjVMiI1oBhWTwkJrV7kR9j4kteVbTaYA0dQKe3NGLb+q243cX5GPBuFgvfZOEkJDORCeEEEIICWtOB1C59scgOvGrl2rj2FrophMlUDi5et/HZcXoFsZD6jZjkvHHnjzhjslGZ575MGVdjrVQNjohhAwbNRX1fUmXQeqibzveiQv+vhVP7dJCHJcJIc+Naeka/HxuJi6dlIzCpIghA+gMiUiAcfEqrChKwkVJduh2fQSpkIcjzX342esluOvd/ejos4z1d0cI8RIKohNCCCGEBIKGXYC5B5BFAWlnz4oivtFiFuG/LZHsmLnhHcAE1ZlsdMYU/RaIXHTjyxgIEDD+vqG/MR4hhBDPMHW5249xY8pE9w0mE53RegAwdJ78cpvOgrve248bXivBiQ4DFCIetNvfZQPgc3NioJSOvJiDTAhot76Nf10cywbjBXweu4pryTM/4L09DVTihZAgQEF0QgghhJBAKuWSdxEgCJje72Hp5bpY2N18JNoaYWupOO33qmST0SOMhcxtQrFxh9/mGGjuXZLDZqN/f6ydstEJIWQ4uqoApxUQqwBNpr9nEx6Y5q3xE7lxzWY4nC68uq0GS57ewvb44POAm+dm4KXlcdDteB9iwdj90RFSAR5eOR5f3j0XRSkR0Fsc+MNnh3HNK7vRojWP3R9ECBlzFEQnhBBCCPE3pi9M1Xc/BtGJ33RZBfigKYodTzLtPeP3mWz0kv5s9KmGzRC6uOZi4S4nTsXWRmf8c8sJf0+HEEKCR/P+Hxte8ilE4zM5S9hNadl+rHhhO/78TTmMNicmp0Xiq1/NwyMrC6EQe+/fgykJ89mdc/HwivGQiwUoqe3BRc9tw/dH27z2ZxJCRoc+oQkhhBBC/K27GuitBfgiIGuRv2cT1l6vj4XFxUex2oQke+NZ96mQTYFWEAO5y4gi406fzzFQ3bU4h91+e7gV1Z0Gf0+HEEKCQ0vZj0F04jM9yYvxO/svcUX5fFS06REpF+Evl03Ep7fPYQPcvsCUdPn5vEysvXc+ilMioDPbcet/9uGRL47AYnf6ZA6EEM/RWmFCCAlAFicPe3sV2N6txFG9DLVGCXrtAthdPCiELsRL7BintGK6xojFsX1Ildn9PWVCyGgcX8dtM+YCEpW/ZxO2dHY+/tMYzY7vyu5ASfXZ93PzBNijWooLtB9gmmEzDinmwsEXI9wVJKqxtCAeG8rb8a8t1XjqymJ/T4kQQgJfy0Am+hR/zyQsMLXHPyxtxJNrbdA6F7Nfu3q8HPdfPhdRCv+cy9OjFfj49jl46vtKvLy1Bm/tqsfeul78a80U9vcIIYGBguiEEBJAqgwSvNMQjc9aNdA7zl58T2vnQ2sXotIgw1dtkUB5MmZpDLgpvQvUj4aQIFXVH0TPvcDfMwlr/2mIYT9785RmLI3tQ8kg+1bIp2GW/ntEOHsw0bQLZcqFPpxp4Lr7vBw2iP5ZWTPuXZKL1Ci5v6dECCGBy2EF2o5w46TJ/p5NSPn2229RXl5+2tdazAJ82SxDo5kLhWUL2vFXwYsQ6Aqw9vOWM96jtrZ2zOf10zmd6sIEIG6+Bs+X6HCstQ8X/f0H3DMjEjOSpWfdPyYmBmlpaWM+R0LI2VEQnRBCvETksiDRVo94ewPUjl4oXHomfxEu8GEQREAnjEGrOAMCuCCMTsGX0ovx1I7ck8cz2eYLYvSYHmlEtsKKGIkDIp4bBicfLWYRDurk2NmjREmvArt7lewrEWpI005vgkcICXBWPVDfXxJkHAXR/bkC6M0GLgv9jsxOtqnYYFw8AUpUS7FM+xGm6zfikGI2nDzKRp+UGol5OTHYfqIL/95ajT9f2t+4jRBCyJnajwIuOyDTAJoMf88mJBysaWW3Dz300Mmv8aVKRMy7HqrJF4PHF8BlNUG7/R1cyFuHqRdL8MO+cqx5651zvmd7RztScgtHNa++nk52u2bNmiH3FSijEXPJ/UDKePxlRy90uz+BduvbgNt12n4yuRwV5eUUSCfERyiITgghY0josmGcuQx55jKkWY+Dj9MvdH7K6JbgeOIVOPiLf6CKJwQPblwQ14c1ad2YE2U4ZxCHKeWyKNaAe9GBZrMI7zdF4c36GLQ6oxB/7eOotBpR4HBCIhzDVvKEEO+o2cLdQEdlAdHZ/p5N2Pq8VYMumwhJUhuWJ2g9OuaYfDpm6r+H2qnFBOMeHFTO9/o8gyUbnQmif1TahF+dl4t49dkz6AghJOydrIc+GeAN8fSWeKS5i0lcAmZfeClycrLRKkxCrTgTjv4H3bGOdmQ7j0MyKwdKfhyAjzA3XYTbf3UXTG7Jae91+OAhlG3bgL4+7j1Hw2zoY7fLb3sQeUVTh9yfWWF8WOvECb0AEbOuQPbCyzAjxgFZ/+1de0M13n3yPnR1dVEQnRAfoSA6IYSMAYnLjCmGLSg27oDMZTz5dZ0gCi3iDGiFsTAK1GwWutDtgNKpRZtFgL8al6PJHQvwgKX8Utyn3oi8lCwgapzHF9LJMjv+N7cdv0jvwu1bBNjjzkebU4H3SxqxoigRMcrTLwYJIQGGSrn4HXOj+kpdDDv+eXoXRHwPj+MJsVe5FEt0n7DZ6EfYbHS6vJ6ZGYVp6RqU1vfila01+L8V4/09JUIICUxUD91r1JkTcUwzhX1AzogW27Eg2oA0tsrYj6t/e9q3IMrRgRXZPFTJT8827+5oR/9jjjETnZTucVY7Exo/3q7HhvIOdFmBLZ0yXDQhASkaKpVGiD/QVT4hhIwCk2lebNiG2fp1J4PnTOD8iGIWKmWToBPGnnGM2w3s1Sqw26CAGzzIXQZc3PA8nsg7BpHFBRw7CESmATnLAMWZx5+LRuzEKv4+fPH2G0hf8xh0ZuDDvY24cEICsmOVY/p9E0LOraGhgc0K8ojbjQnHvgWTG3WclwX9/v0e1cwMFGMxR2/UGx2uzZ0qVBulUAmduDqlZ1jHHlXMxAz9eqhcOhSa9rBNRn2lV6tFayu3bH00PP559VBjYyMuTANK64H/7KrDvGgT1BIPn0z8BNV7JYSEtJYD3JbqoY+ZdmEC4q95DBXqYsAGSPguzI4yYKLafNZVvjXSQkQZOpBlOYoqeeD9O+TGqxCjkuCbQ63oNtrw3/3NmJMdjXjqhUWIz1EQnRBCRmh8LB8PRXyFLB1X365bGI9d6gtxQloEN+/swQKbi4fvO9RssIaRrzRDULMTT3+4E/l3LcItU6RAUwmgbQD2vQFkLgRSZgxreae16SimSrvQKMtEY4+ZveBaUhCHwqSIMfrOCSGDBdALCvJhMpk92n9SAh9ltylhsLkxYcVtsDnP3EdvMCDQtPbomQU0HtX19JTd7oC/vFzHPbC8LqUbKuHgZbh+isk8L1Wdh8W6z7hsdPlMNkPdm8xm7udr86ZN2FrSH4AZBaeBe3AwFgF55v9AfkEBzCYTEm78O5CQg9X3PwfttnPXmh0M1XslhIQsmwno6H8YnUyZ6KNVrpfiqeMJ2KgpglQD8NxOFEdaMENjhExw7ohzjXQ8phk2I8NSDp7bdc77OH/SyMW4enoqNlV0oKJNjx3V3UiUCcGTKPw9NULCCgXRCSFkuNxu5Gp/wP5bFZAIO2HhSbFDvQKHFbPg5p27BrnBwccXrZHskkIB3Fgc24dCtQWbq7nAkcUtAjIXAIlFwPH1QE81ULMZ6K0DClYBIpnHUxTxXLi0OBkbKzrYzu7MEkCHy43ilMgx+SsghJw7o5cJoL/zh6tQkDb0SpIE/SHAcBAOVSp2/XPxab/3bUkVHnp9PSwWCwKN1mABczv6jzvPx+yiH5dEj8Sr35biX1/ugdPpnyD6QZ0Me3qVEPLcuCm9e0TvwXz+z9BvgNrZi/GmUnY1kjdZrVZ2Oy0vGdMnTxj1+1VUHsdXBwGt1rNa8EP9H2AC6Nff/zc4YtKxuwuImXs1brjqMo/L5Aygeq+EkJDWdhhwOwFlPKBK9PdsglaNUYxnTyTgqzbuPocJhPcdWo/5aSIszBm6bEqLOBMWnhwytwmJtjq0SLIQiEQCPs4fH4+kSBl+qOxEq5mPxBv/jppeO+gRDCG+QUF0QggZDpsR+PJXmNH5KSDk4YAtFSVpP4dRMHhwuscmwGetGhgcAsgFTqxM0CJBeo6AkTQSmHAF0HoQqN4A9NYCZW9zX5NHezxVPp+HpQVxkIr42N+gxZbKTkgEfOQnqof7XRNChokJoE8Zlzz0jvs3spvI9AmYknj6/uUN3CqXQJaTpPHs+xxEUkkV/OmV/iz0VYm9SJTaR/QeTp4Ye1VLsEj3OVvahWk46hrkoepYUcklSIwe/Wd6u3LsG3/Gp2UjOacAVXsa0GO0oVOSiBkZUWP+5xBCSEjUQ6emosPWaBLhuep4/LdFAxe7Pg5YkaCF9NgXePq7jyG55U6P3odJgqqV5qPAvJ8t6RKoQXQGj8fDxOQIxKkk+HJ/A0yaRDywsQsOVQObqc78PiHEewJvnQohhASqvlbgjYuBI5+yDUJ/s86CZ/XnDxlA77QK8UlzFBtA14gcuDq559wB9AHMBVDSJGDyzwCJGjD3AmX/AfpahjVl5kJqXk4MilO4Ui7fl7ejtuvHxqeEED8v49b3/5+Oyvb3bML2BvzbNu7z8ZcZo6sLflg+G0a+EhHOHuSbSsdohsGNOQdNz9Cw47KGXtidwyuVQwghIa2lv2Ul1UMfllaLCA8eS8bi7fn4pCWKDaAvie3DN7Or8I/iBkQ6h7+qqlbKZawzQfRgEK+WYkmiHaYTJbC7gN//9zDu++QQzGerC0gIGTMURCeEEE90VgKvLgFaD7DZ4BtS/gfP7rYxIYJBD+uwCvFpiwZmFx9xEjuuTO6BWjSMIIIyDphyI6BKAhwW4NAHXL30YQYxFo6LRUGiim1quvZIK7oMXCkAQogf9dZwW0UcIFH5ezZh6Y2GGPbme360HgWq0ZXNcfDF2Kc8jx3P1G9ga7ESYFycChEyESx2Fw436/w9HUIICRzN/ZnoVA/dI702AR4tT8TCbXl4tzEaDjd3/v5s5nG8NqWOLZM5UnXSfDZJKtrRjgjH2Dbb9hYxH+j89E+4fqKKbZj6yb4mrH5xByVMEeJFFEQnhBBPskRevxDoawZixgG3bESnLGfIw5gSLp+3aGB18ZEoseGyxN5Bm9qck1gBFF0NRKYBThtw+GNA1zTsQPqS/HikRMpgd7rx1cEWmGz+a+JHCGE+JPqD6JSF7hdMn4qPm7nyIr9IH5vSOYcUc2DiKxDp7EK+uT84EuaY0mLT0rls9P0NvXBQNjohhACWPqD7ODemTPRBudzAh00anLc9D280xMLm4mOGxoCPZpzAf6bVYnKkZ83cB2Ply9EszmTHmZZjCB5uXF6gxDu3zESMUsw2HV31wnZ8d2T0jcIJIWeiIDohhAymaR/w1irA3MPVK/z5OiCKu8AaTJ+dj89OyUC/JEkLyUgC6AOEEmDiVYAmE3DZuUC6vm1YbyHg83BxUSKbEdhnceD7o+1wM6nphBDfY/7vMU2DGR58ppCxx9RQ1TsEyJJbsSDGMCbvaedLsF+5iB0ztdGZ5mYEKEhUQykRwmh1ss2uCSEk7DGrWxkRaYAixt+zCVhH+6S4oiQb9x9NRa9diHylGe9Mq8GH02swQ2Ma0z+rJshKupxqTnYMvrlnPltCTW914PZ39uPRr47C6qBVcYSMJQqiE0LIYBno/1kNWPuA9LnAjV8C8qGbopkcPK6JqJOrgX5pYi8k/DEIVvOFQOFlgDoFcFq5QLpleEvjZSIBVhQlQsjnob7HhL31vaOfFyFk+AztgN0ECMSAenSNOcnIstrebOAaNd+Y1sUugx4rBxTzYObJEeXoxDhzf5AkzDEPcaf2Z6Pvq++Fi/kHIISQcHayHvokf88kIDndwAvVcVi1Oxf7tQooBE78X14Lvpp9HPOiDV7pwzpQFz3FWg2Ra3Ql3vxVJ/29X87CL+dzyRlv7KjD5f/aiToq70KIf4Pof/3rX2G320/72qZNm7Bw4cKxmhchhPhX1/H+ALoOSJsNXPeRRzWLrS4evmjVQGsXQiV0YnXSCEu4nItABEy8AlDEAnYjcOQTwDG8+uYxSgkW5cWy493V3WjRjn4JJAl8dO4OML213JYp08QX+Hs2YWdrlxI1Rin7OX158tg+TLTzpdiv5P5fzdR/D1A2OqswSc0+yGVWQlV16P09HUJCFp3vgwTVQz+nLqsA1+/NwtMnEuB087A8XouN8ypxS0YXRF5MA+0VxaFXEAsBnEi3ViIYiQR8PLh8PF67cRoi5SIcae7Dihe248uD/Y3sCSGjMqKPoAceeABm8+lBl/Hjx2P37t2jmw0hhAQCfTvwzmWAuRdIngpc/zEgUQ55GJNYt7YtAh02EWR8F1Yn9kIl9ELwRCgFJlwJiJWAsRMo/5IrDTEM4xPVyE9QgTnq+2PtsDkoyBPq6NwdoEF0pkQT8bk3G7il80yzZ6UXPqcPKOfDwpOxDcpyLYfG/P2D9cZ+UmrkyWx0KidGiHfQ+T7YMtGpHvqpyvVSXLI7F7t7lZALnHhmYgP+OakBCVLf9HKqkY5nt1lBVRf9TEsK4rH23vmYkREFg9WBe94vw+8/PQSzjcq7EDIawuHsvHXrVnbLXPTu2LEDCoXi5K+///575Ofnj2oyhBDid1YD8N5VgLYBiMryOAOdsa1biXqzBEKeG5ck9UIj9uJFilQNTLgCOPAO0FMN1G8HMuYPq9Eok43erDVDZ7Zj24lOtvEoCT107g5ATIPggebAFET3uRqjGFu61ODBjRvTur3yZ9j4MpQpF2C2fh1m9a3DCWmRV/6cYFOUEoHS+h50GWyo7zYhI4b7PCKEjB6d74OIsQvQ1nPjRCrnMqBRkIwr9mTD6BQgQ27Fq5PrkKMc3orb0aqRFWKq8Qe2uWiw9zVJjJDhvV/OxHMbj+Mfm0/gg72NbIPvf98wDZl0/iXE+0H0G2+88WTw5fbbbwefzyWyM9ucnBy8/fbbI5sFIYQEAqcD+PgmrtGPPBq4/hOPG/0c6ZPhgI67GDk/Tod4iQ+yJVQJwLgLgYqvgfodgCpxWIdLhAIsK4jHf8ua2aV+ObFKpEfTBVWooXN3AGIe0jE3ZtIIQMbViSa+83Z/FvqSWD3S5Tav/TlMEH2yYStiHG3IM+/HBq/9ScFDKhJgYnIE9jdoUVrfS0F0QsYQne+DSGMJt43JA2TcCp1wJ82YjE9ll8DhFGCWxoCXJtUj0psJSefQIs6ChSeF3GVAvL0BwU4o4OO35+dhVlY0/ufDA6hqN+CSf2zHC9dNwcJxXHlPQoiXgui1tbUnT8SHDx+GWq0ezuGEEBK4mGXl3/4WOLEeEMq4DPTobI8ObTKLsLmTy1ZnLvpyfZkxET8B6GsBWvazwXQZZg3r8NQoOYpTInCwSYfNlZ24fqaMXXJPQgeduwO8lIs3OmORc+qz8/FxM/fg4qb0Lq/+WVa+HKWq8zCv7xvM6VuLf2O2V/+8YDE5VYMDjVp2JRTTkyMpUubvKRESEuh8H0Sa+oPoqTP8PZOAYBRpEHf5Q3DwRFgc04d/TaqHdCx7Sg2DiydAvbQAeeay/pIuUoSCuTkx+Oaeebjjnf1sSbWb3yjB/Rfm49YFWeyDN0KIZ0YUKbngggsgEolGcighhASmfW8A+94EeHzgiteBlGkeHWbmSfFNWyRc4GGc0oIZGj90P89eAigTAIcFcwUHwR/mddCc7BgoJUK2rMue2h5vzZL4GZ27A0hPfxA9ikq5+Np/WzTsMvEchQVzowxe//PKFPNh5KsQ4ezBBZr+pfthTikVoiCRC+4xN/KEkLFF5/sgykRPnYlw120ToC5yGnhCMbIdNfj3ZP8F0M+oi24+ilASp5Ky5V2unpbK9vJ6Ym0Ffv3hAVgdVCedEK8G0deuXQuZjLJGCCEhonEv8O3vuPGSR4D8iz07TiDCMckEWFx8xEnsWBar809SKV8AFKwC+CLE83tx/1zxsA4XC/lYnMct52Pq5HUZfFt7kPgGnbsDhEUHmJmHVTwgMt3fswm7BUfvNEazY6YWui8+rx18CXarzmfH18Qch5ziWqypadxqgJouI7rpnEPImKLzfYBz2oHmfdw4zDPRTU4evmjVwMkXwdJ0DCvM30LM93/T6TpJAZsgFetoQazQhFDClPP8y+UT8cdLCiHk8/D5gRbc9PpeGG3BXf+dkIAOon/++efIyMiAQCA4+WKWjTFbQggJKvp24KMbAJedC0TPvdfjQ6OW3gqDQA0p34Xl8VoI/VkFRR4F5HKBmv+3SIJkd9uwDs+KVSI7VsEGmbYe72SbUJHQQufuACvlok4GhKGxRDhY7O5V4IRRCrnAiUuTfJcBfUQxC1pBNDRCK+6ZObyHnKFKoxCzfTgYlI1OyNii832AazvErh6FNBKIzkW4YjKh17VHQO8QQOwwovPTP0GEwMiItggUaBVnsOPpqg6EGqZ8y89mZ+DNm2ewq5F31XTj4S3d4MuoBBQhQxlRyIdpVHLttdeioqICNTU17IupwcZsCSEkqDJBmEai+lYgNh+49EWP6xPv7xVBNekiNrXxwngd1KIAeHofPwFNrliIBTzc4v4QPPfwmpvOz42FgMdDY48Ztd1+KEtDvIrO3QFWykXD3ZwR3xnIQr80UQuV0Hef2S6eELvUF7Hj++dKoITZZ392IJuawWWjV7br0We2+3s6hIQMOt8HwQrYgSz0/uav4WhPrwINZgmEPDcytPvgsugRSGqkhex2hnJ4iUnBZF5uDD64dRZilGLUah2Iv+YxWAPjOQYhAWvEn9q//OUvkZubi/T09NNehBASNNY/DDTsBCRq4Op3AAnXHHQo5a19+KJZzo7T7bVIl9sQEHg8lDgLobW4kYVGTGl5f1iHR8hEmJQWyY63He9iM0RIaKFzt5+5XYC2jhtHZfl7NmGlwypkM94Ya1K7ff7nV8gmo9aiRqSUh8slO33+5weiBLUUKRoZe64pa9D6ezqEhBQ63wewxj0I91IuLRYRSnoV7HhJbB+kTu/3KBmuGukEdlss74YyhBeRTUiOwAe3zoZGyoc4LhNbO4Sw2CmSTsiYBtH//Oc/41e/+hW6u31/E0IIIWOici2w+0VuvPolIMaz5ZR6ix13vLMPDjcP5pp9SLf3B8QChBlS/GadhR3Pafg3Is3Da2Q3PUMDmUgArcmOan34ZseEorE4d3d1dSEzMxN1dT/+3B85cgTTp0+HRqPBfffd53EpoE8++YS9oU9KSsL77w/vgU/Q6msFHFZAKAFUCf6eTVj5qCmK/dyeEmnEeDX3GelTPD7e6sxnhyvFJVBZWn0/hwA0LZ3LRj/SooPZRjfthIwFulcPcGHeVNTuAtZ3MGVDeChQmZGv8sM52QM9wjj0CmIg4rtwfrYQoSwnTok/LY6GQ9+NPjsfXxxogd0ZAKusCQlAI4qQvPvuuygpKUFaWhqmTZuG88477+SLEEKCIpD1+Z3ceNZdQP5yjw996PMjqOs2IULkQtfXTzOtAQPOGwfsOIJxELqsOP/En7ns12E0m5mTzZU8KNcJqDZeCBntuZsJoK9YseK0ALrVasXKlSsxdepUlJaW4tixY3jzzTeHfC8m8H799dfjoYcewrp16/Dwww+jsrISYVMPPTKDDaoS33C6gfeaotjxDX7IQh9QaojDploHxDwn5tb/02/zCCRpUXLEqSRwuNw42ETZ6ISMBbpXD2C6ZqCvibsGSJqCcLSrRwmtXQilwImF0YFVwuU0PB6qZVw2+iV5oR1EZySphOj46CGI+G609VnwzeFWuGhZMiFnGNGnwU033TSSwwghxP9cTuC/vwTMPUBCEbD0EY8P/e/+JraDuYDPw9WpRhwy9yFQvc67An/h/R3JfQdQ1PYpDiVe6fGx45PUONSkQ6fBioi513l1nsR3Rnvuvuaaa3Dddddhz57+ZcgA1q5dC51Oh2eeeQZyuRyPP/447rrrLtx8882Dvterr76KxYsX45ZbbmF/fffdd+M///kPmz13NkywnnkN6OsL3P97g9L2rwyheug+talTjRaLGBqRAxfF6/w4Ex5++70F+25VoqBrHcqSrkW7iqu5Gq6Y5mZMNvq3R9pwsFGLKWkaiP3apZuQ4Ef36gGsqT8LPX4CIOGaK4eTTqsQB3Tyk2VcJILADtIyJV2mGbZgea4Q/3WEfma2vasBc2Md2N4pRn23CdtOdGHhuFh/T4uQ4A+i33jjjWM/E0II8YUdfwfqtgEiBXDFG1xZBQ/UdhnZLHTG/yzJRVRb/0VwgOrmRWF7xl04r+ZvmFv/L1TFLINFxNU7Hwqfx8OCcTH4dH8zVJMvQovegfDMlQktoz13v/LKK2wpl3vvvffk1w4ePIhZs2axAXRGUVERm40+FOa4iy7iGi0yZsyYgT/+8Y/n3P+JJ57Ao48+iqDmtAF9zdyYgug+9U4jl4V+VXIPpH6+YT/Q5sImexGWig9hYe2z+GjiKx43tA5V2XFKtieHzmzH0RYdJqdxJV4IISND9+oBLIxLuTDV/rZ0qeAGD+OUFmQoAqSn1CBaxJnQOUSIltsx3tiAcBAtceOCwgQ2E/1AoxbRCjFbN50QwhlRqgefz4dAIDjrixBCAlbjXmDTY9z44r8BMTkeHWZzuHDP+2Uw2pyYlRWFOxd7dpy/HUq4HB2KXEidesyt76//7qEUjRwJUhd4fAE+OhrASy2Jz87dTAD9p5iM8FO/zmSVMu/X29s76Hv99Di1Wo2WlpZz7v/AAw+wGe8Dr8bGRgQdXRNXWolpZCz17IEWGb0Gkxhbu7im0del9iAQvGNdBDtfimT9QeR2b0K4Yx7cDtRG39+ghZOWjxMyKnSvHgxNRcMviF5pkLKrwoQ8N+YFchmXU7h5fOw1xLPjmcIqhAumRvrsLK6855bKTrT3BWbdekKCJoheW1uLmpoa9sXUNWXqnxYWFuK9994b+xkSQshYsBm5Mi5uJzDhCmCS52VKnvq+EoebdYiUi/D3qyez5VyCgZsnwJbM+9jxxPbPEWeoGNbxhZFck7dtDRYcbw+Oi13i23O3UCiERHL6ag6pVAqTyTSs44Y6htmXCbSf+go6vXU/ZqGHeeaxL33UrGGz3uZH65EuD4ystx63GvuS17DjefUvQOAKjHn5U36iCgqxAAarA5VtdL4hZDToXj1A2c1A60FunDoD4YSphLKjmytfM11jgEoYPKVR9hi4RvAzRVVcOn2YmJ6hQXasAk63G2uPtMHqoObfhIw4iJ6enn7yVVBQgBtuuAEbN27Es88+S3+rhJDAtP5hrqmfOhlY/rTHQayd1V14eWsNO/7bFcVIiJAimDRHTEZ5zAXgwY3FNX8b1sVfpNgNU+VOMEf8feNxr86TeJ83zt1RUVHo7Ow87Wt6vR5isXhYx3lyTNAbqIceme7vmYTVTfvHzVwpl2tSAiMLfUBp8g0wiGIQaWnGpNYPEe6EfP7JMi6l9T1wh1GggpCxRvfqAaqlDHA5AGU8EJmGcHKoTw6DUwCl0IkpEYMnWgSa/YZYmO1uJPC1iDZVI1wwq0uXFsRDLRWy5dY2lnf4e0qEBIQx69wjk8nQ1tY2Vm9HCCFjp3oTsPdVbnzJPwGZZ6UU9BY77vv4EDu+bmYalo3nlvMFm20Z98DGlyFJfwj5nWuHdax2+7vs9ptDrShvDdJmjsRr5+7p06dj165dp2W/MQ1AmSD5cI4rKytDcnIyQjr7zNDOjTUURPeVrd0qtFtFiBI5sDQusD6/7AI5dqTfwY5nNr4GhfX0h1HhaEKymm0q2muyo6bL6O/pEBJS6F49kOqhzwirFWk2Fw+lvQp2PFNjRLD1jra6hdhQ42DH2T1bEU6kIgEumpAIZhH28Q4DqmhlMiEjC6IvXrwY55133snXokWLkJWVhXnz5o39DAkhZDTMWuDzu7jx9F8C2Ys9PvSxb8rRrDUjNUqGBy8uQLAySuJQkvpzdjy/7gWIHJ4HJ+xd9ZiTymXfP7s+fGoBhiJvnLsXLFjA1jd/44032F8//vjjWLp06cm6q1qtFk7nmcs/L7/8cnzwwQc4fPgwDAYDnn/+eVxwwQUI+Sx0eQwg5pYzE+/7oIl7mLM6qRcSfuBlNh+LW4FW5QRInEbMr3sO4U4iFKA4hWtetreOstEJGSm6Vw9QYdpU9IBODrOLj0iRAwUqM4LRF5UDQfQfEG6YVdjTM7jrqc0VHTBaub8LQsKVcCQH3XTTTWcs9WAyyJiTNCGEBJS19wP6FiAqC1j2qMeHMRcJH+xtZBNFnrqiGArJiD4uA8b+pOtQ2P4lNJZGTG9+CzvT7/T42KvHK7GryYLvj7XjcJMOE/uDHCS4eOPczdQ2f/XVV3HttdfivvvuY5uZbdmy5eTvazQaNst80qRJpx1XXFyMe++9F9OmTWProefm5uLOOz3/mQw6vfU/1kMnPmHkybGpk6udf3WAlXI5icfHpuzf4bqDN6Kgax0OJ6xGc8RUhLPilEi2uWh7n5V9iE0IGT66Vw9AzEPBgaaiKTPCKgt9v1bOjmdpjBAEaQL+11UOMD2vEwzH2JVjRkkswgkTRGdWiHXqrdhc2YEVRUn+nhIhwZWJfuONN7Kviy66iG1SwmyXLFnCnqA91dXVhczMTNTV9TfaAtjGJ8wSb+amm7kZ9zQD5ZNPPmFrviUlJeH9998fybdECAlF5V8Dhz5gAxW49CVAzC0lHIrWZMP9n3JlXH4xNxMz+7uTBzMnX8yWdWFMaXkPCluXx8emRohwSTF3sfTsBspGD1Zjce5mMOfmjIwfg8GrVq1CdXU13nrrLZSXl2P8+PGn7fvTAPqAxx57jA2wM+fwnTt3skvNQ5a2/1qH6qH7zFFRARxuHqZEGjFOaUWg6lAW4FDCZez4vJq/gs/Uyw1jzAPr8Yncw4/S+l5/T4eQsD7fkzHUVQWYugChFEg6+3VRKDrSJ4O1Pws9V2lBsGo3ulHl5MoOZvVuQ7gR8Hk4f3w8W9alutOImk6Dv6dESHAF0Zml26tXr0ZiYiLmz5+PhIQEXHHFFezXPQ2gr1ix4rQAOlNDdeXKlZg6dSpKS0tx7NgxtpP4UJjA+/XXX4+HHnoI69atw8MPP4zKysqRfFuEkFBi0QHf/JYbz/kVkOb50slHvjyKDr2V7Uj+vxfkIVRURy1Ei2oiRC4rZjb214j30D1LctkLp00VHTjaovPaHIn3jPbcPRjmvZYvX47o6OE9cGIC7suWLQvtpqLMZ5GZCQbywq6RmD8dFhWy26uTAzQL/RQ70u6AWRiBGFMNNRllHvSmRTL/W1DfbYLORkE/QgLpfE9GqG7bj/XQhRKEA6cbKOvPQp8SaWLvI4LZHse4sC3pwohRSk42AN9S1Qm70+XvKRESPEF0Zsm1y+VCY2MjzGYzu3U4HB4vxb7mmmtw3XXXnfa1tWvXQqfT4ZlnnkF2djZbV/W1114b8r2YZeRM3bdbbrkFEydOxN13343//Oc/I/m2CCGhZMOjgKENiMoGFv3B48O+PdyKLw60sE/cn75qEttQJWTweNiefjc7nNj2OSLN/SUmPJAVq8Ty/qV7L/1Q47UpEu8Z7bmbjLIeuioxbG6c/U2SUohevgYKgRMrEgL/oZ9VFIFtGb9ix7MaXwn7JqORcjGy47jeAVX6IOtAR0gAoPN9AKrbwW3Tw6cufaVeCoNTALnAiQJl8Jfn2uPgEqtStXuH1V8qlMzMjIJKKoTe4kBJbeAnKRDiDSMq8ssEvPft28eWT2Ew22effZbNIvfEK6+8wpZyYeqhDjh48CBmzZoFuZx7WllUVMRmow+FOY5ZojZgxowZ+OMf/3jO/ZmMd+Y1gJ7IExLYdu3ahZqa4QVtY83VOL+Jewi3QbYC7R99yo6Zzxem9NO5aC1O/H4dV+ZkdZ4Crs4a7D9HLKO2thaBrFerRWtr6xlfb0UiJsqnocBUiqmVz+Cd+N8PumqIwZToYCyKs+MrAN8casFFyXYkKId/ComJiUFaGmXj+sNoz91khKgeus8pi5axWyaArhAGR6bU0biV7MPNRMMRLKp9Gt/k/wXhbGqaBic6DGg08iFQBX9JNUJ8ic73AYYpUVu3nRtnzEW4fMtlOi6uMynCBGEIPA8t14rQFZmIGEcrImrX4rBy+P+WWq0WwUwk4GPRuFh8dagVZQ1aTEyOgFom8ve0CAn8IDoTANm0aRN+/vOfn/wa8+vBglOnYgLoP8UEs0/9OlOzTSAQoLe3l62Rfi4/PU6tVqOlpeWc+z/xxBN49FHPmwsSQvwbQJ8zh7lA8aw/AkPEB8puUwBxArxeZsMvHv2Tx8fGrn4Q8nGzYWuvwdN/+w2e9qA2rd0eWPVru3RcZsTmTZuwteTAWffZrpBj+yygyLgTpR88if19Z/+Mtfc0s9s1a9ac/FrclY9CljUVN/z5TfSs/9ew5yeTy1FRXk6BdD8Y7bmbjPAucqAeuob+nn3ByRNCnj8vsBuKng2Pj43Zv2ebjI7r3oiK7i2ojl6EcJUQIUVypIxtLqqausrf0yEkqND5PsB0nwCMHYBAAiRPQzhosYjQZRNByHNjojq4s9Atdie7LS3dh/edMvwqHRAffAcvHz067PeydXBJWCaTCcEqM0aBVI0Mjb1m7KjuwkUTEv09JUICP4j+3HPPsbVPP/roI2RlZbENxZimYN9+++3IJyIUQiI5fZmzVCplP2AGC6L/9LiBY87lgQcewG9+85vTgvCpqakjnjchxHu4DHQ3Fqy8Ehnpnv0/XSUrQ6F8H/pcUpSmr8HP7payXz9aWY1967/A/VfOwlVLzszE2aJPwvNdxRDChWemtCBj1m2D/jmvfluKf325B05nYAXR9WYbu51bmIKiiVxN4LPZb9NhmusgXp7VgpdFy9hSLz+1e6cd28qBBdfdg2lzuWBOp4WHrR1AxJSLcc3KZZAOo9pNe0M13n3yPjbDnYLovueNczcZAtNEzGYE+EJAzTWkIt6llSaCL5Ii2tmNyRHBdZPaqcxDacoNmNH0Js6rfhJNEVNhFaoQrqama7gg+qSLYLQFx4oCQgIBne8DtB56ynRAxN2XhLqD/VnoeUozpALPk6ECkc3BBdGLs+MgyM0HbDVYmdiDsox1/m1aAADW2klEQVQpcPGGV/aTvbc6Dlht3P1aMGKSXefnxuK9kgZUtRswKdWMxAiZv6dFSGAG0Znaanv27IFKpWKX9zO1x5m65cxT7tdffx3JySO/QYyKimKbhJ5Kr9cP2WyMOa6zs9PjY5iA+0+D9YSQwMYE0CcVnTsgPEBj78AlHVz29fboK5GbOuXk7/UaLNjHXL/GqjBl3OmfVa0WEd7YwTWL+XVuOy7LUjPrWgb9s5JKqhDIIuRiJEaf+3s44FiFSe1HkOOqxUxlKxqk+Wfso5Zxn6UR8SlIyeX+/pPdblSVNqGtz4IOUTzmZMd48bsgY8Gb524yBG0Dt1WncIF04nU9Mu6B60T7UfB4kQg2u1NvQU7XJkRZGjC/7jlsyPk/hKuMaDlUIhf0kGN9jQnzZ/l7RoQENjrfB3g99IzwqIducPBRbeTiLcURwZ2FfiqFVARbTCFMbQrIXUZMVXWjSZIzrPcYuLcKdrEqCcYnqnGstQ87TnTj8inJbHCdkHDgcXWqw4cPIy8vDytXrsTXX3+NlJQUNqubqTH8l7/8BXPmzPGohvm5TJ8+nS3dcGq9YaZ2ORMkH85xZWVldIFASDhyu7FE+xGEcKJWko9K2WRPD8PvjqRA7xCgOMKE2zLCo6GbXhiFQwqult9s/TruL8IDzAXStAxuddDBJh2s/dkZJDB5+9xNPAyiR9LKC1/otAphFkXC7bRjvIPr5RBsnHzJycD5xPYv2AZm4Yo534xTcRnoXx83wuagbHRCzoXO9wEqDOuhH+mTwQUeEqU2xEoCa8XuaLl5fNRKucSibPNhhLNZWVEQ8HnsijGmtAsh4cLjIPovf/lL9tXR0YGHHnro5NcPHTrE1i2/9tprccstt4x4IgsWLGBLq7zxxhvsrx9//HEsXbqUrYs+0ITB6TwzWHP55Zfjgw8+YC8cDAYDnn/+eVxwwQUjngchJDgVmkqQaquGnSfGpsgrzlqe5Gzea4rCtm4VJHwXnp7QGBKNbzxVqjoPDoiQZKtDmrXS4+OyYhTQyEVsQONIMzVnDmTePneTIW6cdRRE96Wjem45sen4bsjdFgSr5ojJOJBwBTteWv0YhM7g/V5GK1XhgkPfjR6zC18c4Pp0EELOROf7ANVZCRjaAKEUSJmBUOdyA0f7uHNxUZDXQj+X6oEguuWIx0lIoUglFbGNRRm7qrvhDuO/CxJePA4XMaVWrrjiCvD5Zx7CBLpvu+029iQ9Ukxt81dffRV33303YmJi8MUXX+DJJ588+ftMXXQmUP5TxcXFuPfeezFt2jQ2A52Zy5133jnieRBCgo/cqccC3ZfseKfqQvQJoz06rsEkxmOVXDOU3+W2IUdpRTgxCiJwSDF7RNnoTK1aRlljL5zMFTMJSN4+d5Nzkzp0gN0M8EWAipoueRuTpFyh52rNGg5+j2C3I/0u6MVxiLQ0Y179PxCuBDxAX8qd31/ZVkM36YScA53vA1TNFm6bNiss6qE3msUwOAVsclKOIjQfANdL8tgkpAhnD6IdrQhn09I1EPJ5bJnPuu7g6kNDiNeD6ExW+K9//Wu2IdxPGY1GPPLII5g7d3hLlJgL4YyMjJO/XrVqFdv45K233mLruI0fP/60fSdNmnTW93nsscfYMi6ffPIJ2zRFJqPGBoSEk4W6zyF1m9AuSkaZcoFHxzBx3/89kgKTU4CZGgNuTj/zsy0cjDQbPT9BDblYAKPVieMdeq/OkYycN87dxDNKWzs3iEgG+MNrPEWGr9YkgdXFh8hphqX+IIKdTajE+v6yLpNbP0Sadg/Clf7AWsiEPLaB2ZbK8Ci5Rshw0fk+QNX+wG2zFiEcHOtfEZavtITs6l4HX4IGaS47zrYcRThTSIQoTuH6z+yppWx0Eh48/mhjmpEw5VQSExPZemvz58/HokWL2MA2kznOPP1mGpeMVkJCAttNPDras0zSAUzAfdmyZUM2IiWEhJYMSznyzfvZ2nsbIq+G28Mu6a/Xx6CkVwm5wImnJjSCH6a9UEaajc7UwBu4aCpr0NJFU4Dy1bmbnElla+MGEVTKxRfK+2/cNeZmwB0atbPrNbNxIOFKdnzB8UchsesQjtw2E5Zlydnxv7dW+3s6hAQkOt8HIKcDqN0WNkF0i5OHagPXUHR8iJZyGVAtnchus81HEO6mpEey94XtfVY0UW10EgaEnu7INPhkmpQwmeJMI8+WlhbY7XZERkayJ2emWQl15CWE+JLQZcV52o/ZcZlyITrEqR4dd8IgwV+PJ7Dj/8trRarcjnC2V7UERcZdJ7PRG6T5Hh03IVmNkroedOitaNVZkBRJq4ACDZ27AyATneqhe53JwUO9iUuiYMqfhJJtGfcgTVeCKHM9llT/Bd/mPe5xz49QsmKcAt+eMGF3TQ8ONWlR1P8QlxDCofN9AGrZD9j0gEwDJBQh1FUapHCCh1ixHXEh1lD0p2qkhXCDhwR7AxROHZuUFK7kYiEmJKlxsEmHvXU9SI3iHnoTgnAPog/Izs5mX4QQ4m9z9N8hwtkLnUCDXaoLPTqGyVj/7eFU2Fx8LIjW49qUHoQ7k0CNg4o5mGr8gc1Gb5DkeRSkYS6a8hNUONrSh7JGLQXRAxidu31rfCwfIpeV6qH78Mad+WyPl9ghdRrZrzV29mF/1egD6i1d/m2e7BBI8V3uH3H14Z8jr3sDajoXoCLuIq/9ebW1tdi/f/+o3oMpyTjWOuurMC81EVvqzfjLF/vwv7O5vhwjwWTlpqXRwy0Smuh8H4D10DMXhEVZt4EVYeNVoZ+NbBKo0CpKQ5K9nm0wekgR3qWSpqRpcLhZh8ZeM9p0oVkLn5ARB9EJISQQxNkaMdnA1RncFHkF7Hxu+eBQSsTTcLBPDrXQgb9OaAzHhL5z1kYvNu7sz0avQoM0z6PjJqVGskH06g4D+sx2qGUir8+VkEC3OKP/ZpnqoftERf+Ne4HKjCYD16Phrx/vYV9jxWzzX1Zdu2o89qTegjkN/8bimr+iWX32HkGjYdBzDwseeugh9jUm72kwjPo9+nq4Guhr1qyBKCYdSb/4J3bUG/Hxg9fDoetf7TFMMrkcFeXlFEgnhPgmiB4GpVy0dgHarSLw4Eau0opwUCObwAbRs8wURGfu//ISVChv1WNffS+KKa+KhDAKohNCgg7P7cRS7Yfgw40K2WTUSX9sQjwYUVwmdopnsuNHC1qQIA3tpYbDzUY/rJiFycZtmG7Y6HEQPUYpQapGxmYeHGzSYn5urNfnSkigW5TRf3lF9dC9rssqQIdNxJ4PxiktqLFyGVCzLrwEc6YWj/r9N2/dibJtG2B1OOFPJSk3IaN3J5L0h3Fx1YP4CvPH9P2tZi5zcMF192Da3NEFfMpLfsDat56DxTL6bDSzgQvuL7/tQeQVTcX2DhfaLQLM/9+XMTlq+P8m7Q3VePfJ+9jmixREJ4R4jUUHNPY/yM1ajFBXqZey2zSZDQphaPQl8aQu+ry+b5BqPQ6RywI7n/s7COdsdCaIXt1pQE6Sv2dDiPdQEJ0QEnQmG7Yi3t4MC0+OHyJWe3SMC3zELP8NXDwBzo/T4dJErdfnGWz2KRejyLgDadbjiLfVe3zcpLRINoh+pKUPMzOjIRZ63LOakNDjdmFhen/2OdVD97oKA5fulKmwQib4scGxOioGKenpo35/ZeTYlyYZCTdPiLXj/ozrD1zPBtJ/phThSy/8ORHxKUjJLRzVezCB6rEWnZTOzmtutAn/LWtGg0mIpZNzIRPTSg9CSIBmobscQHQOEJWJUMaceasMXAA5TxU+pTx6hHHoFcRC4+xEhrUCx2Vjv0osmDCJVWlRcjT0mHBCT+dmEroo0kEICSpqRzdbC52xNWIVW5POEx2KHIjjMiFzmfDY+GYq43IWeqEGFfJp7HiGfqPHx2VGKxAhE8HmcKG81b/1gwnxN6m+HrEKPpw8AdVD9zKXmynlwt24F4TBjXufNAnf5z7Mji9X7sfFueGXC5OikSFOJYHD5WYbjBJCSEA6vp7b5ixDOKxm7bULIeC5kaUIj1IuLB4P1TLuoXO2+Yi/ZxMQJqdxTb/rDHzwxNRglIQmCqITQoKH243ztJ9A5LahUZyDo/IZHh3WZhGiQ8E1WVpq3YzYEO8YPxp7leex3eZzLIeRKuZqCw+Fx+Nhcip30XSgSQu3+8dsUELCjar7ALs1iuKoHrqXNZrFMDoFkPJdyJCHx417dfRilCVezY7fulSKaAHXSDVcMOebqelcU9GDTTo4nOFRNoAQf2DKHmVmZqKuru7k144cOYLp06dDo9Hgvvvuo2u+s2H+Tk70J6PkLkWo65ZwCQOZcisk/PD6eaiRTmC3mZZy8N3+LfsWCNKj5IhSiOFw86AsCv2ffRKeKIhOCAkaeeYyZFor4IAQGyKvZDMAhuJwAd93RLD7Go9uQZ7jhE/mGqx6RfE4IZ3Ijq+M9vzvqiBRDZGAB63JzpZ2ISRcKbu4ILpeEu/vqYS88v4sdKYWuiCMVhdty7gHx+1xiJHz8b8x28Puxj0nVgm1VAiz3YljtPqJEK8F0FesWHFaAN1qtWLlypWYOnUqSktLcezYMbz55pt+nWdAaj8K6FsAoQxIn4dQ1yNOPHkuDjct4kyY+ApI3SYk2WoQ7pgH3cUpEexYNekieshGQhIF0QkhQUHiMmKR7jN2vEe1DFomy9MDO3uU7BJDodOCng0veXmWoWGvagm7XRjRjPQIzyJTTB30ggQ1O6Yl9iRsuVxQdh9khwYxBdG9yebiodo4UMolvB7cOfliPKm9EDqLG4XSTszr+xrhhM/nYXIal42+v0ELF92kEzLmrrnmGlx33XWnfW3t2rXQ6XR45plnkJ2djccffxyvvfaa3+YYsE70l3LJnA+IQrvZpDg+G1aBHEKeO2xWhJ3KzeOjVkolXU6Vn6Bmfx5E0ak42mnz93QIGXPhV0yREBKUFui+gtxlQLcwHqWq8zw6psksQpmOq8eW0ncY1RaDl2cZGtrFaaiXjEO6tQr/O0eC7z08bmJKBA4161DTZYTeYodKKvLyTAkJMJ0VENl0MNrcMImi/T2bkHbCIGGXC2tEDsSHYYmuNmcEbvrCjM+ulmOqYQvaRGmokk9GuChMUmNPTTd0ZjtqOo3IiVP6e0qEhJRXXnmFLeVy7733nvzawYMHMWvWLMjl3LV1UVERm41+LkzmOvMa0NcXJitHwqgeujxvLrtlAuiiMUjPbOzsw/6q5lG/T0uX737WqqUTUGgqQbblCH5wX+rRSulQxiRWpSpcqDUIsK7ahJ/5e0KEjDEKohMyiIaGBnY541iJiYlBWlramL1fuMgXtmCCaQ87Xh95NVw8oUdZius7mMxoHgpVJgjaOxHoxuLCcawuGveqlrJB9F9MFmG3xehxV/bkSBmatWYcae7D7GwKIpIwU7ed3exodCImneqhe1O5XsZu81XmsL1f/bzCgU90hbgi4ijO136AblECukXh0cxWJOCzD2731vVif0MvBdEJGWNMAP2nmCD4qV9nSjcIBAL09vayNdJ/6oknnsCjjz6KsGLqARp2ceNxFyCUuU8JoueMsqGoycD1Yfrrx3vY11gx27z/kJ1JPHJAhAhnD6IdregWJSHcZSm5IPqeZgs69VbEqiT+nhIhY4aC6IQMEkAvKMiHyTR2y8TlchnKyysokD4MEgFws5ILTB1UzEWr5MyL+rPZ1q1En0MIldCJ+TEG7AzgUujeuHAc7UUj07i1yhyJcTItLnbuQztOX9J7LkUpEVwQvUWHGZlREPDDNLpFwlPdNnazpc6BK0K/DKrf9Nn5aLKI2Vv4/DCswXqqd7TFmB5nZx96rux+A+/F/Ro2PveAIdQVp0Rif70WrToLWnVmJEaEx/dNiL8IhUJIJKcHw6RSKUwm01mD6A888AB+85vfnBaET01NRUirWge4XUD8BECTjlDWK4iGKCoZPLcTGaMMotus3Ll81oWXYM7U4lHPbfPWnSjbtgFWh/d7hjj4EjRIc5FlOcaWdKEgOhApdsPaUgEk5ePjfY24c1GOv6dEyJihIDoh58BkoDMB9Hf+cBUK0mJH/X7lDZ1Y8/hH7PtSEN1zDy6QIEHQBwM/AtvVyz06pt4kxpE+bqnpsjhdwHeKH8sLxzG7aOTx8Fl3Fu5P2Y+LxaV422mBUzB0XcfsWCXkYgFMNieqOw0YF68a3TwICRYuF1C/gx1uqXPiCn/PJ4RVGLjPohSZDWqRC+HMBT6+1dyA6zufgcbZiQt738OXUTcDvNBve6SQCJGXoGKbi5Y1aJE4kYLohHhTVFQUjhw5ve6zXq+HWMw81DwTE3D/adA95FV+w23zLkaoq5VwgdEIexckY3TKUUfFICV99A8flJHl8KVq6UQuiG45ghL1+T79swOV/sB3kCTl4/2SBty+IJvtZ0JIKKAgOiFDYALoU8Yl+3saYSnC2oLfz+UuzDdFXuZRdp3FOVDGBSiOMCFVZkewGIsLx7G8aNyhT0Sd1oWMSBMKOtfiSMLqIY9hMs8nJEWgpK4Hh5p0FEQn4aOzAjB1sw+bSlvCpO6rHzA9JCv6S7kUhHkW+gCLQImvom7G1Z3PszfwM/QbwuYmfnJaJBtEP9FhYOujR8ioFwch3jJ9+nS2VvqA2tpatuY5E1wnAOwW4MQmbpwf+kH0BnEWu42ytQMI72bqNdJCuMFDgr0RCqcWRkEkwp2pfBvkl/wajT1mbDvRhYXjRp+USEggCP00FUJIcHK5MLPjXYgEPOyzpaNaVuTRYZu7VDA6BYgUOTA3iiuTQkae4fjcHq6r+pSW97jlqR6YkKxmaxQzZV26DaNb3klIsNVDN0ZNgD28k6O9qsMmRK9dCCHPjRwlfb4M6BCnYmMkt/5hjv47pFt8m4XnL0wvjrQoOVub90Cj1t/TISSkLViwgC3J8sYbb7C/fvzxx7F06VK2LjphnipsBexGQJUEJE5CKGs2i9AtioPb5USkrQPhziRQoVXMJUJlW476ezoBwe2wYnEGl/Tw7u56f0+HkDFDQXRCSGAqfQ2xllr0Wd34j3GOR4dUGSSoMsjAgxsXxOnGpEt8uHttvw1GtwTR5jpk9u706BiVVISsGAU7ZrLRCQmneuj66NHX8iTnVqnnSrlkKawQB3ipLl87ppiJQ/LZ7Dnw4p53EOEYu8bogWxKGpfxd7RFB6vd+/VvCQnnmuivvvoq7r77bsTExOCLL77Ak08+6e9pBWApl4vYsoihbGMnt+rX2lwBkZtLuAl3TDY6I8t8esmjcHZ+FldedWNFB9u7hJBQQCEmQkjg6WsBNjzKDh/YaEGviwvIDsbo4GNz/wXddI0RCVLvd2MPB3obsM42mR1PaXnX4+OKUrigRnlbH2wOSssl4VMP3RAT2tln/uRyMw9LuSB6npJuxs5mS+RlaBWlQ+o2YUXPmxC6Qj+4wWSiRyvEsDvdOEKllAgZU263GxkZGSd/vWrVKlRXV+Ott95CeXk5xo8f79f5BQyXEyj/mhvne9bDKZgNlM40n9jj76kEDKYuOiPVehwiF5WbY6RGiDAjIwpOlxv/3d/s7+kQMiYoiE4ICTzf3gfY9OiUZuKlUrtHNXI3dKphcfERJ7Zjhsbok2mGi69t0+HkCZCmK0WcocKjY1I1MmjkIjaoUdFGQQ0SHvXQIZLDFJnn79mErCazmC3XJeW7kC4P/eDwSDh5QnwdfROMfCXi7M1Yov2YO0mGMB6Px9ZGR39JF+ZmnRDiPQkJCVi+fDmio6P9PZXAwTxIN3UBMg2QuQChTO/gY3cPl+BkOr7b39MJGD3COPQKYiGEExlWz+6XwsEV01LY7af7mtiHcoQEOwqiE0ICS/lXQMXXAF+Ikrjr2MzDoRzVy1BnkkDAc+P8eB0Eob2C0ue63BGoil7Kjqc0v+NxUGNicsTJki500UTCoR46UmfCzafGht5S2Z+FnqO00Of8IAyCSHwTdSPb12K8uRTFxv6fzxCWl6CCXCyAwerA8Q7qh0II8bGjn3Pb/BWAILSvA37oUsHu5iPC0QNHb4u/pxM4eDxUyyaww2wq6XLSxRMTIRXxUdNlRBn1LiEhgILohJDAYdFxWeiMufdCK0ke8hCdXYCtXUp2PCfKgGgx1UP1hv3Ja9htXtcGKK1tHh0zPlENIZ+HbqMNLVpa1khCvx46Mub5eyYhi6kKdcIoYcf5Svo8GUqzJAdbI1ax44W6z5FkrUEoE/L5KO4vI1bWoKUHt4QQH5dy+Yobj78UoW5zp4rdptlq/T2VgFMt5YLomZZy8N10T8pQSoS4sDDhZDY6IcGOguiEkMCx/hFA3wpEZQEL+oPpg2Cy1L/vULPZEMlSGyZFmHwyzXDUocxHQ8Q08OHE5JYPPTpGIhIgP4G70D7UTJkHJPTroSNjvr9nE7KY1UY2Fx9KoRNJ0qHLfBGgTLEAFbIpEMDF1kdXOEO70TOz+ol5cNuht6JZSzXzCSE+Ur8TMHYA0kggayFCGXPvxWSiM1Jtdf6eTsBpFWfAxFewfUmSbKH98Ho4Lp/KlXT56mALrA56uECCGwXRCSGBUw5h3xvceNULgEg25CFlWjlaLGKIeC4si9OBT8v7vWp/0vXsdmL7ZxA7DB4dM1DS5USHARa6ZiIhXg8dSVwTXjL2Kk42FLUwK6aJJ3g8rI+8Cp3CRChcejaQzneHbtNtmViAgkSu2d3+BnpwSwjxkWPhU8rlmF6KLpsIcoET8XYq5fJTbh4ftdJCdkwlXX40JzsGCWop+iwObCzv8Pd0CBkVCqITQvzPbga+/BU3nnqzRyURuqxC7OrhyrgsiNEjQuTy9izDXq1mDrplGZA4jShs/9KjY+LUUsSrJWzmSr2RTjkktOuhQyj292xCktXJYzPRB4LoxHMOvgRfRf8cFp4MSbY6LNR9gVA20GC0tsuIXiM1nyWEeJnTDhz9jBtPWI1QN5CFzpTQZFY5kXOXdMm2HAn5xt6eEvB5WD2FK9NKJV1IsKOIBiHE/7Y8AfTUAKokYNmjQ+7u7C/j4gQPmXIrClUUVPEJHh/7k65jh5NbPwDPw4zGgWz0WoOAeROvTpEQn6N66F7H1EJ3unmIFjkQIw7dTGpv0QljsDZqDdzgYZJxO8YbSxCqNHIxsmIU7Hh/Y6+/p0MICXXVm7jVaIpYIHMRwiWIvjCGGjifS71kHBwQIcLZg2hHq7+nEzAu7w+ib6nqRKfe6u/pEDJiFEQnhPhXSxmw8x/ceMUzgJQLuA6GyUDvtIkg5buwJLaPlvb7UHnsRTAJIxFhbUVWT3/wcAjj4lUQC/kwOniQZkzy+hwJ8Rmqh+4Tlf2lXMapzPR5P0J10vHYpbqAHS/RfoI4WyNCPRu9vFUPs43qiBFCvOjQR9x2wuWAQIhQ1mfnY5+We0i5iILog64Aa5DmsmMq6fKjnDgVilMi4HS58eVBKgVEghcF0Qkh/l0C+cWvAKZ7OXPxmXfRkIc0mkXYp5WzYyaArhDSUkJfcgqkOJzALVf1tMGoSMBHQX+DUeWkC706P0J8iuqhe53BwUejmSuTQ6VcRmePahlqpOMhhJ2tjy52hWbzzeRIGeJUEvZGnZpaE0K8xmoAKr/lxhOvQqjb2aNkV4VlKSxIlVOD78FUSyf+WNKFnNFglEq6kGBGQXRCiP/sfB5oPwzINMCFTw65u8XJw/ftTKY6DxNUJuQoaSmYPxxKuBwuCJDatw8xxuMeHTOhv6SLPHcWesyUGUhCBNVD97oqNgudh0SpjXpfjBaPj+80a6ATRLHLzJf1fhiS9Vp5PB6mpGnY8cFGHRxO+rkhhHhBxTeA3QREZQHJUxA2pVyiKQt9KDXSQraEWoK9EQonPcwdsLIoCSIBD8da+1DR1ufv6RAyIqG95ogQEri6jgNb+gPnTABdGTvo7sx9/sZONQxOASJFDraZKPEPgyQex6MXI697Aya1fogNOf835DExSgmiJS50WwXYUGPC0rk+mSoh3kX10H1WyoWy0MeGlS/DN1E/w9Wdz2Oc5SAaTTtxSBF6H8g5cUooTwhhsDpQ0a7HhKShS8URQoJLQ0MDurq6xuz9YmJikJaW5vkBB9/7MQv9J7XGxmpu5eXlCATMfdgWqofuMZNAhVZxOtvQO9t8FIeUoXeeHQmNQozFeXH4/lg7vjzQgvwL1f6eEiHDRkF0QojvuZzAF3cDTiuQswwoGnoJ5DG9FCeMUvDhxoVxOohoHY1fHUi6hg2iF3R+h+3pd8Mi4mrQDiZLyQTR+dhQa2KX2TOd2gkJWlQP3et6bQJ0WEXgwY1cCqKPmXZxOrarV2Jh3xdYqP0cLeIMhBrm/DIpNRLbT3ShrEGLwkQ1m6FOCAkNTJC6oCAfJtPYlaWSy2UoL6/wLJDeWwfUbGFXSmHSdV6fm95ggD8dN0rQahFDwndhVpTRr3MJFtXSCWwQPctyhILop1hZnMQG0b861IL7LsijczMJOhREJ4T43q5/AI27AbGKayY6xMnTzJNhZ3/2w+woA+KlDh9NlJxLi6oI7Yp8xBsrMKH9C5Sm3DjkMclyF3Y36tCFCGyp7MCSgnifzJV415tvvombb775jK+/8cYbuOmmm855XFFREQ4fPnzy17/4xS/w6quvImhQPXSvq+jPQk+X2yAXhF7ZEX/ar1yIVOtxZFmPYXnPW/iCNw2hZkKyGntqu9FjtKG+x4SMaK4hHiEk+DFZ3kyQ+p0/XIWCtMFXs3qivKETax7/iH1fj4LoZe9y26xFgCbda3P7tqQKD72+HhaLfx8kb+nk7sNmRRkgpfOxR2qkEzC/72v2XCtyWWDnc9c04W5JQRzkYgEae8w40KjF5P7ya4QECwqiE0J8q/0YsOnP3PjCJ4DIIS5U+QKUS8bD7uYjRWrDlEjTqP74xs4+7K9qxmi1dIV5HTceDweSrsYFxx9FcevH2Jd8Pdy8wU8pAh5gOLIRETMuw7t7GiiIHiKuu+46XHrppSd/bTAYMHnyZMyff+7sbJPJhOrqanR0dEAkErFfk0gkCCpUD93rS8cr9VTKxWt4PKzTXIs1HU8hytGJ2xKO4FOEFolQgMKkCPYmfX9DLwXRCQlBTJB6yrhk36+oPdAfRJ9yg1fnxgT3A6oeOpVy8ViPMA69glhonJ3IsFbguGySv6cUEORiIZYWxOPLgy346mArBdFJ0KEgOiHEd5x24LPbAKcNyL0AmLxmyEMi514LvSCCXT54frwOI60AYjJwF31//XgP+xorZlv4ZsVXxizD/Lrnoba1I7t7K07EnDfkMYYD37FB9M2VHWjqNSFFI/fJXIn3iMVi9jXgxRdfxOrVq5GdnX3OY8rKythM9NjY0WeP+Q3VQ/eqdqsQOocQQp4bWQpqIu0NFoESa6NuwJVd/8SyyEZckidEqLXgnJwaiYONWjbjrVNPP0eEkDFQvQnoawZkGiB/BUKd0cHH3l7uIeTCGP+WlQkqPB6qZRMwzbAZ2eYjFEQ/xariJDaI/vWhFjy4vIBKfJKgQkF0QojvbP0b0HaIu+hc9fyQZVxqDEKoZ13JjpfE9kElHPntvc3KZTLOuvASzJlajNHavHUnyrZtgNXhRLhy8iU4FL8as5pex+TWDzwKojt6WzAxTozDHTZ8uLcRvz0/zydzJb7BLDd+7rnnsGfP4A+qSkpK0NTUxAbR7XY7rr32Wvz9738/Zza61WplXwP6+vy8EoTqoXtdpUHGbrMUFoj5tHTcW5ol2ShVLsZ0wya8slKK37pCK0CilonYJqPHOwwoa+zFeG7hCyGEjNz+t7ht8bWAMMhW0Y3A7h4FbG4+UmVWZMnpYeRw66IzQfRMyzHw3eF7z/hT88fFQC0VokNvRUltD2ZnR/t7SoR4jFrzEUJ8o3kfsPUpbrz8GUCVMOjuXQYrPmqUg8cXIMHeglzl2Fy0qaNikJKePuqXMpKWnjEOJVwOJ0+AlL4yxBoqPTrmgmwu+/yDvY2wO0Mt7zG8vffee5g5cyYyMgZvVFhZWYl58+Zh+/btWLduHdavX49nn332nPs/8cQTiIiIOPlKTU2FX1E9dK9yuYEqAxeYyKdSLl63S30Rai1qxCr4uFv6DVdLJ4RM6V8qXtmmhzl8F48RQsaCoQOoXMuNJ5+7lEso2d6tZLfzow1D5T+Rn2gVZ8DEV0DqNiPJVuPv6QRUubWLJiSyYyYjnZBgQkF0Qoj32c3AZ3cAzBP4wsuACZcNurvL5cZvPzoIvYMPW1c9cmxVPpsqGR6jJA7Ho5ew40mtH3l0zPQkKWKUEnZp/YZj7V6eIfGll156CbfffrtH+73//vvIy8tjg+4PP/wwPvnkk3Pu/8ADD0Cn0518NTY2wq+oHrpXNZrFMDkFkPJdSJPb/D2dkOfkCfFUy2RYHW7MEB1nm0WHkoQIKRIjpOzDmWqDwN/TIYQEs4MfAC4HkDwNiB+PcLCzhwuiz40OrZVKvuDm8VErLWTHTEkX8qOVxUnsdu2RVkqqIkGFguiEEO9jGol2VQLKeGD500Pu/vK2GvxQ1cnWwu364q8QhFyV1tByIPFqdpvf+R2kdu2Q+4sEPFw9PYUdv1fS4PX5Ed84ceIE+1q2bNmwj42Li0Nz87kb/jJlXtRq9Wkvv6J66F5VaeAaiuYqLWxDYuJ99VY1HtzErfhaWPsMIsxNCMVs9BoDHzxR6JdfIIR4AbNKZ//b3HjKzxAOOq3Ck+XVZkdREH2kJV0Y2RYmiB5aK71GgynhwiRVaU12bD/e5e/pEOIxCqITQryrbgew65/ceNULgDxq0N331ffiqXVcWZAVSWbYu+p9MUsyCq2qiWhTFkDotmFi22ceHXPN9DR2Sei2412o6zJ6fY7E+z766COsWLECItHQRYdnz559Wjb5rl27kJ6ejqBA9dC9yuFisoW5IGcelXLxqWd323DEkQaxy4yl1Y+FVFmXrFgFImQi2F08KCYs9fd0CCHBqHEP0H0cECmGXFUbalno41VmRImppvdI1EvGwQERIpw9yJDo/T2dgME0E11RxJV0+YpKupAgElBB9DfffBM8Hu+MF/P1wRQVFZ22/y233OKzORNCBmHqAf57K/fUnakbOO6CQXfXmey45/0yOFxudonXNA0t4w8KPB4OJF7DDovbPgGfWeY6hNQoORaOi2XH71M2ekj47rvvsGjRotO+ptVq4XSeedNVWFiI2267jW1A+tZbb+Hpp5/GHXfcgaBA9dC9qtYkYRuYqYROJEnt/p5OWGHKnTxnXgk7X4I0XSkKO75EqODzeJicGsmO1dMvgZP5ZgkhZDj2vsptJ6wGJCqEg5399dDnUBb6iDn4EtRLx7HjWao2f08noKws5oLo6462wWKnhzQkOARUEP26665Db2/vyReTpRYTE4P588+d6WUymVBdXY2Ojo6Tx73wwgs+nTch5CyYDLav7gH6moCoLODCJ4bY3Y3ffXoQzVoz0qPleHz1BGpeE0SqYpbCKIqCytaBnJ7NHh1z/Uwu8/jjfU2wOujCKZiZzWY2ID5nzpzTvq7RaHD48OEz9n/qqafYEi2LFy/GI488gr/97W+48cYbERSoHrpPSrkwWeh0DvC9drcGO9O4vgYLap+DwhY6S6zHJ6kh4rsh0iShtHVsmpUTQsKEvg04+jk3nv5LhIsdVA99TNT0l3SZoaQg+k9LrSVHymC0ObG5osPf0yEk+ILoYrEYkZGRJ19vv/02Vq9ejezs7HMeU1ZWxmaix8bGnjxOJuPqdhFC/Kj0daD8K4AvAq54fciMjbd31WPd0Xa2XvYL106GSjp0SQgSOJx8MQ4ncEtbJ7V86NExi/Ni2WZvPUYbvjtCF5XBjDnvWq1W5Ofnn/FwbNKkSWfsz5yrP/vsM/ZBeF1dXfBkoTPqtnJbqoc+5hw8IeqMVMrF38qSrkG7ogBSpx6Lap5CqBAJ+MhScj1WvqikgBAhZBhK3wBcdiB1FpB05nVNKGowidFkFrM9qmZoqPTiaNRIC+EGD+NkOiSpKENgAFNFYkV/NvqXVNKFBImACqKfymKx4LnnnsMf/vCHQfcrKSlBU1PTySA6cyPO3MifC/N7fX19p70IIWOs/Riwrv//7tL/N2TJgwONWjz2TTk7fuCiAhSlcEuuSXA5lHA5nDwhkvUHEWfg/j0HIxTwcfX0VHb87h4q6UKCpB460+eBQfXQx1yPOAFO8BAttiNGMnRZKOIdbp4Q63MehAsCjOveiOzuLQgV2Uon3E47Krrs2N/Q6+/pEEKCgcMG7HuDG89kylSGhx39pVwmRZigEHIPIMnImAQqtIq5Fbir8oT+nk5AWVWcxG43VXRAb6EyfiTwBWwQ/b333sPMmTORkZEx6H6VlZWYN28etm/fjnXr1mH9+vV49tlnz7n/E088gYiIiJOv1FQugEMIGSN2M/DJzwGHBchZCsy6c9DdmSzkO9/ZB5vThQsK43Hz3MH/z5PAZRTHsGVdGJNaP/S4wSjTWKaktgfH26nZDglwneWAuYfqoXtJt4S7kaIsdP/rVOahNOUGdnxezV8hdoRG5rZMCBiPcQ8FXt1W4+/pEEKCwbEvAEM7oEoEClYh3Eq5zKFSLmOiur+kyyV5tNr6VOMT1Wzzb6vDhfXH2v09HUKCN4j+0ksv4fbbb/dov/fffx95eXls0P3hhx/GJ598cs79H3jgAeh0upMvpu46IWQMfXsfF2hSxAGXvgTwz/0xwzT2uveDMrToLMiMUeBvVxazy7pI8DqQeDW7zetcD5l96Cy/hAgpluTHseP3qMEoCXRUD91rBMpo9Amj2TEF0QPD7pRfoFeaBqWtE3MaXkKo6Cvh6hozZcQauk3+ng4hJNDt6f/8m/YLQBAeAVCm9/KugXro1FR0TIPo52UKIAP15RjA3PsPZKN/RSVdSBAIyLUkJ06cYF/Lli0b9rFxcXFobm4+5+8zjcyYFyHEC/a9BZT9B+DxgcteBpSxg+7+3Mbj2Ha8C1IRH/9aMwVqqoMe9NpUE9CmHI8EwzFMaPsce1NvHvKY62el4/tj7fh0XxN+d0E+ZGKBT+ZKyICGhgZ0dQ3dQDHzwFfQAGgWZ6F9//6z7lNePnQpI3ImecF85k4KiVIb1CJaNu4v+r4+tLa2nvz1x5pf4tbWh1Dc+jE28+egTZI55HtotVoEMntXPSYlSHCgzYrXd9Ti/60q9PeUCCGBqmkf0FwKCMTA1JsQLqoMUnTbhJAJXJgcSQ8bx0KvMA7NVgWSJUZMEVb7ezoBZWVxEv6+gYsL9Bpt0CgoUYUEroAMon/00UdYsWIFRKKhA2qzZ89m9x8oy7Jr1y6kp3P1pgghPtRSxmWhMxY/CGQvHnR3pgP38xuPs+MnLpuI/AS1L2ZJfJSNfuHxR1Dc9glbDoCprzuY+TkxSI2SobHHjK8PteDKaVRmi/g2gF5QkA+TyTzofswamY77lICcjyvvex67ms5dOo6hN1Dm1nAoChay23zKQvcLi93JbktLS1FWcXqZk7iJibg0vhUzjzyK5ftm9/9vODdbRy27ZRoHB6pL8hRsEP3DvY34n6W5iJTTDTsh5CxK/s1tJ1w+ZHJQKNZDnx5phJjv9vd0QgOPh92GBFwuqcZMYRXK/D2fAJIdq2TLuhxr7cN3R9tw7Yw0f0+JkOAKon/33Xe46aabzshqUalUEAhOz1AsLCzEbbfdhkceeQQVFRV4+umn8c9//tPHMyYkzJl6gA9/BjitwLiLgHm/GXT3xh4T/ufDA+x4zaw0rJ6c4qOJEl9g6qLPr3sOKlsHcrp/wPGYJYPuz+fz2Iulv35XyZZ0oSA68SUmA50JoL/zh6tQkHbuG2SpvRcxXV/DyRPgnw/+Em7e2VdMfFtShYdeX882SCee0QoiIYnNBc/tQi4F0f3C5uCC6MXZcZgz/fR6/8fcubjY+g/M1fTgxYtkOCCYOOh77d5px7bjgNVmQ6AqihOjIFGN8tY+trH1XYtz/D0lQkigMXQAR/7LjWeET0NRxs6BUi5UD31M7dHH4/LoakwTncBBlwMufkCG4/yWjc4E0ZmEKgqik0AWcP9rzWYz9uzZg5dffvm0r2s0GpSVlWHSpEmnff2pp57CzTffjMWLF7OlXP72t7/hxhtv9PGsCQljLifw318CugZAkwmsHrwOOpPtdue7+6Ez21GcGomHVoz36XSJ9zn5YhyOvxSzml5nG4wOFURnXDk1Fc+ur0JZgxZHW3QoTIrwyVwJGcAE0KeMSz73Ds2tQBcgiEzF5LxzX9yXN3R6Z4IhrFqSz24j7F2QCSjjzZ8UUhESo3+6MkyNvfplmNv3LVY516MnZirsfOk530MtEwdFDdZbF2Ti1x8exBs76nDL/ExIhFRKjBByin1vAi47kDIdSJ6CcOFwAXt6FOyYguhjq8IchQ6jC3EKC5L7ytAYOd3fUwoYK4oS8eR3FdhV3Y1OvRWxKirBTAJTwDUWlclksFqtyM/nbqgGuN3uMwLojMjISHz22WfsktG6ujrccccdPpwtIQSb/gSc2AAIZcDV/wFkkefclfl//P++PIrDzTpo5CK8eP0UumkNUYcSLmczdlP6yhBj5Mr2DIa5ULqgMIEdv7eHGoySAKTt/7mMoOyYseR2M0H0PHYcbaWGUoFqn3IRtIIYKF06zNSvRyhYUZSEBLUUXQYrviijnz1CyCkcNmDva9x45u0IJ4f65DA4BYgQOjBeNXipOzI8LvDwdZWDHWf1bPX3dAJKapScTbBjmtquPfJjbxZCAk3ABdEJIUGk7F1ge39d4FXPAwmDL/F+Z3c9PtjbyJSEw3PXTEZypMw38yQ+Z5TE4UQUVxefyUb3xHUzueDk52XNMFi5C0xCAibSq23kxpEURB9LB3Uy9Akj4bJZoLG1+3s65BycPBG2RF7KjqcYfoDG3oFgJxLw8fN5Gez4lW017IN+QghhlX8JGNoAZTxQsArhZHd/FvqsKCP4g7fAICPwZSV3j5PNBNHpvHOalUWJ7PbrgxREJ4GLguiEkJGp2wF8dS83XnAfUHTVoLszS7Me/eoYO/79hflYMC58mvOEqwNJV7Pb/M7vILHrhtx/dlY0smIUMNqc+PIAZQWSAGLsABxmgC8CVNwFPhkbX7Ryq5fMJ3ZDAK4uNwlMtdJC1EjGs/9O8/q+Rii4ZkYalBIhjncYsKWKSjERQvrt6W8oOu3ngDDwS1SNpd399dBnRVEpF29YX+OA1S1EhLUFMaYT/p5OQFneH0QvqetBq45WQZDAFHA10cnpGhoa2KZnYyEmJgZpaZRBR0b/cyY2NiN/250QuuzoTVqI2ogLgf37T/5+fX09W2JpQK+NjxdPKOFw8VEcYYOicTfefXf3kH/Ojh07RvDdkLGm7+tDa+vwMwJa3XGYJ85Esq0W6SfewR7tBPbr5eXl5zxmYTIfNV3AK5vLkSfsZOvWngt9phHfl3JJAfhUgmqsON3AV21cEN147AcgnmqDBrptEauQ0VGBHMthJFur0SzJRjBTS0W4dkYqXtlWi1e21mBxXpy/p0QI8beWMqCphHtwPvVmhBO7CyjVyk9mopOxZ7IDBxyZmCk6juyeH9ClyPX3lAJGYoQM0zM02FvXi28OteKW+Vn+nhIhZ6AgeoAHNgsK8mEyjc1TOLlchvLyisANOrkcgK4ZMPcA5l7uxTStFMkAoYSrua2MAyJSwy4jIJB+ziKlwM6fKyCMFaCk2YlFj30Fs+Orc+7PE0mQcP3fII5Xw9p2Al89/Tt8ydQZHAadgZ5E+wPTBJZRWlqKsoqaEb2HKUmFf4wHJjR/iOvWVbJfW7NmzTn350uVSLnrbdRqgTkrr4OttSp4P9NI6NDWc9vIdH/PJKTs6lGiyyaCxGWGuXY/sJiC6IGuRxSPI/KZKDLtwgLdl3g/9l6AF9wLW2+em8k2F91Z3Y0jzTpMSKbG1oSEtYEs9MLVgCoe4eRwnxwmpwCRIgfylBZ/TydklTjGcUH07q3Yk3qLv6cTUFYWJ7FB9K8oiE4CFAXRAxiTGcwENt/5w1UoSBtd6Yvyhk6sefwj9j0DIuDkdgH6VqCvGenaOuy/VYHiby4C3J7UQeYB6iRAkwHEFwLJ04CUaUBUFthi28RrP2d8lx05PRuhtHfCxpdDOvkibJ/GZSsMePXbUvzryz2YfeGlyMnJQrmkEJ3CeIjcNsxUt0N6+90ez23v3n0o3/MDzLbhBd3J2LA5uCB6cXYc5kyfPKL3ELonwWg9gXSZCb+aysNjB4H7r5yFq5ZMPecxz3V24gdDMtbc+WvcFXskOD7TSOhizldUD90rPm/hstAzrcdRxTw0J0Fhl/pC5Jv3I8HegHHmA6iST0EwS4qUYUVRIj4/0MLWRmd6thBCwpPQ2gsc+TQsG4oy9vTXQ5+poXro3rTXkQs3eIg3lkNpbYdBEl4PawZz0YRE/L8vj+JgoxaNPSa24SghgYSC6EGACWxOGZeMoMdkmvfUAJ0VQE814LCyX45mXokCLoDOFwKyKECm4V7Mr5k6tMy+dhPQ18r9uq+Ze9Uz5T5e5t6f2T9zAZB7PpCzFFAl+Pf7DbWfMybAceQTwN4JCKUQT7oWRYozg+5JJVzmcGpKEuyp09HZowIfblySbECyLGdYc6qt7w9cEb9SSEVIjFaP+PhjulmYbtiEK2Jq8BiAlFjVoD9r98Sa8UMJsMOUjGcydYgQuUb8ZxMyaoYOwGkFBGI6r4whi5OH79q5jN8cayXW+XtCxGMmgRp7ledhrn4t5vV9g2pZEZy84L6lYLLdmCD614da8bsL86nxOSFhKqb+a8BpA5KnAinnTvgIVbt7+4PoVA/dq7RuJVpVE5CkP4ysnm04lHiFv6cUMGJVEszKimZXhzHn5DsWBXfZOBJ6gvuKlwQHq56rLdd6gAuED2BKtESkocUqw52v7safX/kcE+ZdPHg2OdPB2tgJ9NZzgfiWA0BzKdB6iCv/cuwL7sVIKALGXwJMuByIyvT+9xnqmZgVXwG9tVx9wIlXAmcJoJ+qVxSHqv7GNIti9UiW2X00WRJoDirmYqphMybJ2lAQM/Sy/ymRJuQrzagwyPBZiwY3pXf7ZJ6EDFrKhSklFuRlKwLJ5k4VDE4BkqQ2xNupkXCw2a9chGLjTkQ4e1Bs2Ib9qsUIZkwJlznZ3E37G9tr8X8rxvt7SoQQHxPygZi6L8M2C52th94fRJ+loXro3lYdtZANojN10SmIfmZJF+Z8/NXBFgqik4BDd4PEewztXEB7z7+Ahp1cAF2sBFKmA5PWAHPuZQPcbapifFHpgE2ROHQ5Fub3mbroqdOB4muAi/4C3LIBeKAJ+MV6YOH9QFL/suK2Q8CmPwHPTwJeWQLsfgkw9fjkWw8pzIOL499zKwiYAFLhZYB68JUR4vhsnFBNYkvvTFSbMFFNNc3DmV4YhWop11T0VzOH7mfA/De/LpX7v/puYzT7I0iI35uKUimXMfVFq4bdrkrUMkXaSJBx8MXYob6IHc/Ur4fEFfwBl18u4GqvfrC3EX0WevBPSLi5rEAIsaULUMQB4y9FuDnSJ4PRKUCE0IF8FdVD90UQnZGqK4XYQZn/p7qwMAFCPg/HWvtQ00l/NySwUBCdjD1rH1DxDbDvDaCznMtiZjL4mIuRWXcC2UuAiJSxzehjGo2mzgAW/wG4dTPwvyeAVS8AmQu5P4fJVv/ufuDpfOC/twINu7ngMPEggP4dt4qACXMUrBoyq9/AVyL28ofh4gmRJrNiYYzeZ9MlgeuAcj67/VmRCGLe0L0PLk3shUzgwnGjFKVaqoVH/IQ5f+kG6qFTU9GxorPzsalTxY4vSdT6ezpkhMrl09EpTITUbcY0/WYEu0XjYpEbp4TB6sD7e/ofnhFCwsavZvQnekz7OXdvGWZ2968gnhlF9dB9oVeegR5pGgRuBzK0u/w9nYCiUYgxLzeGHTMlXQgJJBREJ2OHqR9XuxUoeRloP8x9LbYAmHozMOl6IDbfd0vhlbHAlJ8BN34J/KYCuOivXHkXpq7toQ+B1y8AXpwN7HkZMNMN/LlLuHwNtB7kAuh5F3P/hoMwOPj4PmIVhKpoyBx6XByvg4AuwgiAJnEO6myRUIh5KJB2Drm/WuTCJQm9J7PRCfELfRt3bmPKjzGroMiYYGqh29x8jFNakK+kbLdg5ebxsVN9MTuebNwGubMPwYzH4+HW/mz017bXwtrfXJsQEvpk9m7MSxPCzRMA025GONozUMqF6qH7TE3UAnab1bPV31MJOCuKktgtU9KFkEBCQXQydsvdS1/nyrYwDUSZTPPJP+Nqkiv93G1aFQ/MvA24bStwyyZg8hpAKOOy5NfeBzxTAHz7O6Cn1r/zDCRME1GmFE/H0R8z0BMmDnqIwwX86mAaeoSxcBp7kacvhURA2f6kH4+Hb/Tj2OFEWbtHK0Gu7y/p8m1bBHpsAq9PkZBzlnKheuhj6tMWrpTLJYm9Q1ZxI4GtRlqIVlEaRG4bZug3INhdMikZiRFSdOit+HRfs7+nQwjxkThjBbvtTVoUlk3Emfu4vQNNRakeus9UR3MlXTJ7doDPxFDISecXxkMs4ON4hwGVbbSynQQOuiMko+O0Ayc2AgffAyxaQKLmamYXXw+ouaeHAYO5U2e6rF/yT+C3THb637hMeaZWe8m/gRemAB/eADTuRVhjsi6P/hfoqgSYbIzC1UBcwaCHMPHQRyuSsLlLzS5J6/j0T5C4qA46Od0WYxZ6zW5ECqxcY+AhTIwwszX1mYzVgaAbIX5pKkqlXMZMo0mEkl4leHDjsiRaCRb0eDzsUC9nhxONO6FycCuIgpVYyMct87ls9H9vrYbTRckAhIQ8mxEacx077Mi6DOHo1HroBVQP3WdaVRNhEmkgdeqR0rfP39MJKGqpCAvzYtnx14coG50EDgqikxGT2Xu5uufN/UFnplzKtF8AMeOGbhDqb7JIYOatwJ27gBs+B3KWcuVLyr8EXlsKvHY+Ilu2hl89OIsOKHuHC3DyhWzjV/bfcwiv18fgP40xbFBkUd862FqrfDJdElysbiFeP2DjftHs2YXiQDb6e43RoFgG8fmKHF0TN6amomPms/6GonOjDUiUUvPGUNAoHYcGcQ6EcGKmfh2C3bUzUqGRi1DfbcK3h6kWKyEhr/Ug+HChpNkJk2Y8wtHuXq4e+gyqh+5TTPmggQajOd3B31tkrK0oSjxZ0sVN/exIgKAgOhmRqwqFGNe9FjD3AGIlMOFKrmY2Uzc2mDDB/uzFwJpPgTt2AZPWAAIx0LgHWaWPoPJuJWKMlVx2doiT2zqB/W8Dxg5AJAeKrgWiuGyswXzXrsafK7kT3O/HtSHTdsIHsyXB6p8lNq6SS28tYOoecv+VCVqohE7UmiTY1d/wiBCf0LcCLjtX/ktB9dDHAvN//7/9q0ouSwrujGVyup392eiFpr1IEgd3PV25WIib5nBN1F/cUk037oSE+gPzljJ2+Pye0L/fO5c9Pf310KmUi88dj17MbnO6t3BJfeSkpQXxkIr4qOs24WhLcPddIaGDguhkeFxOJB99CR9eIYfA7QQ0mVz2eXQ2gl78eODSfwL/cxiY/1s4RCrkRPGR1lcC7H4RqP0BsAb3jeG5XDeReSjyPWA3AgqmKeuNQETykMft7lHgnkNpcIOHa1O6cWvG0A0jSXir1brZBqOslv1D7q8QurA6kQu2vdcY5e3pEXJmPfRIph46pWWNhf1aOepMEsgFTlwQRzdDoaRVkoEa6Xg2m/P6mEoEuxvnpEMuFqC8tQ8/VNG1DSEhq6sKsOlh50vx8bHwXB11aj10airqe40R02EVKKCwdyNRf9jf0wkoCokQS/K5/nrUYJQECqG/J0CCiFkLfHwT4mu4pUZtikIkTFwees3WmGYySx7GEdUSvHL3Yjy9Mg5Shx5o2AU0lgBx44HUGVywOdhZ9Ujf/wTevUzOPCEBorK5JqIerCg42ifFL8syYHPxcX6cDn8qaKY4E/HIIXM8MiVaoO0wkLFgyJ+361J78HZjDNZ1RKDDKkSchBrvEB+geuhjbqC3wYXxOvYBGQktO1UXI8tyDAsjWlAYG9zXhpFyMa6bkYZXt9ey2eiL8mg1CiEhqb+8YJd8HGzODpSXl4/6LcfiPXzpqF4Gg1MAtdCBfKqH7nMuvgg1UfNR0PkdW9KlVV087PcI5Z9bpqTLN4db8fWhVvz+onzwKOBA/IyC6MQz+nbgncuB9sNwCqS49oMe/P6eKUgItQD6KVxCGV7ca8cvfr4KU6JNXAC9r5n9O2BfTBZ+ygxAkxGcWYpNpcCnv0B0bx3bOKtDXYTECRd59FCk3iTGjfsyoXcIMENjwPNFDRCG7o8CGWON9ghAFsWVg2o/AiRPHXR/5oJ+aqQR+7QKfNyswV1ZlBVIvMzl4D7vGVQPfUxYnDx83RbBjq+gUi4hqVOcjOPSIuRaDuHB+RK83NeH1tbR1RTXav3XfPYX8zPx1q46lNT2YF99D6am02ooQkKKoQPoa2LvfY7aEsHcza1Zs2bM3l5vCI6s7t395RJnaIwQBOEtbSg4Eb2YDaLndm/Gtox7PY4t9PVw90Rj+XNrCLCf28X5cVCIBWjWmlHWqMWUNC4hgxB/oSA6GVpPDfCf1UBvHVsXtmraY/j44avxe4QJJqgck8e9mKAKE0xnlv4xNZ2ZF5ORzgTT4wq4ZpyBzmYCtj0NbH8WcDthlcVj6Ys1eO4Pk5DoQQC90yrEz0oz0WUTIV9pxiuT6yAVUL1QMhw8LnB+Yj2XAZQ0ZciLxetTu9kgOtNg9PZMCqITL+tr4QLpTH8IeYy/ZxMSNnWq0ecQIklqw6woqrkaqvaozmeD6FdPEOKpjVvxckXNqN7P1lHLbk0mE3wtMUKGyyan4MPSRry4uRqv3URBdEJCSusBbhszDu0tPDB3M/+483zMLsod1dt+W1KFh15fD4slOLK6mfKcDDo3+09d5GzY+RJEWFsQa6xCpzLPo+PMBq403vLbHkRe0eBJSUMpL/kBa996LuB+bqUiAZaNj8fnB1rYki4URCf+FgQRP+JXrYe4DHSm2SSTcX3D5zDXhXEGmToZKFzNlbZp3sv9/Rg7gcpvgOpNQMJEILEYkEcj4DCNsY5+Bnz/EJd1wZhwBSpSb8T233NdwYeid/Bx075M1JslSJVZ8fa0WkSIaEk+GYH4CVyfASYbnXlAF8U1cTuXi+N1+GOFA80WMbZ2qcDlsxLi7XroacG50iiAS7lcmqgFn/5KQzobfZs2DvMjO/DY1C5sSlw9qvfbvdOObccBq80/Df9uW5iFj/Y1YmNFByra+pCfoPbLPAghY8xpA9qPcuPESUALt+olJ0mDKeOG7gs1mPKGzqCqh16qpXro/uYQyNhAem7PFraki6dB9AHRSelIyS0c1RzaG6oRqFYUJbFB9G8Pt+Kh5ePBpwtJ4kdUgIGcW9M+4M3lXAA9fiLw8++HDHSFDVkkkLMMmHUXkLkQEKsAhxloKgH2vgIceJcrU+EMkAY1TLbvmyuAT27mAugRacBVbwOXvwqniFvC58lS/FvLMti6edFiB96eWku1qcnIMXXQmUA6o7l0yN2Z1Q6X95eAeLcxAB9SkdBC9dDHVJdVgC1dKnZ8GZVyCXlvtWax22WRTSiIsCIxWj3il1om9uv3khWrxEUTEtjxS1sCN8BACBmmzgrAaQWkkWF9rj+ml7HlOZl66AVUD93vJV0YOT1c/znyo/njYqCSCtHeZ8Xeuh5/T4eEOQqik7NrOQC8sxqw9gFpc4CbvwFUXGdkcgqRFEibDcy6Ayi8HIjO4UpV6BqBiq+B3f8EKtcCPbWA2+X7zPOaH7iVBK+cB9RvB4QyYNEfgLtLgPGXeJxhaXXxcPuBdOzqUUIpcOKtqbXIVPgnK4yEkIFa6D3V3OqOITANRhmbOlXosMu8PTsSrpiHn0w5FwbVQx8TX7ZFwunmoTjChByl1d/TIV5WZY7AV5V2trbuDP0GBLs7FjLXdsBXh1rR2OP7sjKEEC/d6w5koYfxirOBUi4zNCaqh+5ntVHz4eQJEGOqgcZU5+/pBBSJUIALC7kH2l8d6r9GJ8RPKIhOztR2BPjPpYBFB6TOAq7/GJBS8YSh66bnsuVR2IB6xnxAogYcFqDtIHD4Q0zs+AQvrZAionU793frLfo2YOcLwIuzgLdXASc2ADwBUHwtFzxfdD8g8jwAaXPxcNeBdGzpUkPKd+HVKXWYoDZ7b/4kfDBlj5gGvYyWfUPunq2wYn60Hi7w8G0fBTeJlzC9L9xOQKzkGuCSUftvfymXgdUkJPT9aSv3sKTAVIoIRxeC2cSUCMzPjWGbsL9I2eiEBD9DO6Bv4e7fmFKcYWx3L7cimUq5+J9VqEJjxHR2nNOzxd/TCTgripPY7drDbXA4qZws8R+qiU5O11EBvH0JYO4FkqdxAXSJZ+U+Rqu8vHxM3icmJgZpaX4MsDHB8/S5XIY6U1eXWS7YVQmR3YzbpoqBvQ/BXfoIjJrx0MdMgTEyH6bIcXBIo0f2fTpsXGOcum1A1Tqu8SnbGofJlFcAk64FZt89ZCmes9Xvc7h5eKZjEnab1BDznPh93D6Iu7qxf5D74ZYursEJIR5nozMNetsOcQ+fBIMv3f95ehe2dauwwZAKnkgaOp8bJHBQPfQxVamX4EifHCKeCysThl5xQkLD3hYX9hoTMF3Rhun6DdiguQbB7Ffn5WLb8S58sq8Rd5+Xg+RIWg1FSNBqPchto3MBMZeJHY6cbmBvLzUVDbSSLhna3cjp3oS9KTf5ezoBZU52NKIUYnQbbdhV0435ubH+nhIJUxREJz9imvsxmcumLq455ppPAan3Gyi19ujZ7Zo1a8bk/WRyOSrKy/0fEGOyG5hmrMwr93zs2bUdpTu2YGmWAHkxgLLnCPsa0Kp34XCHC419LjTq3Gjqc6HX4gbzoJVp+sJc6ERIgAQlHwlKHtI0Ily+YAIk2mqupt+pUmcCRVcBE68cchVBa2sru13z+EdnzD9m5f9CUZAAt8OOxv/+CTfX7vf42zfbqF468UBUFleP0qLlGjwlTR5094UxemTJragxSaCYcF7ofW4Q/6N66F7JQj8vVg+N2Onv6RAferdnPBtEH2/aixLV+egTBu/KjhmZUZidFc3euL+4+QQeWx3e2auEhERD0SGuOUPdsT6uHrpK6ESBilYZB4LqqIU4r/pJJBjKobY0o086uia3oUQk4OPCCQl4b08DvjrYQkF04jcURCccUw/wzhXc8ra4QuCGz7nmmT6gNXBNTJbf9iDyivprJI+iq/S7T96Hrq6uwAqG8fg4YVLj7rUW/OPO82GPTYTK1gKlrRNyezekjj4kqvjsa1i6j/1YFoPJfM9eDIy7EIhI8fgttFouM3D5VWuQl5t1spx6qSsLDe4Y8ODCHHEtEq9bwSykGvL9Nm/dibJtG2B1ULCEePiwKWkKULOJa4A7RG1Kphn7jWldeKQiGeqpq7BgxmTkh+rnBvE9h/XHeugaCqKPFvPw97NWLohODUXDT7klGvWScUi3VrHZ6Bs1VyGY3bs0F7te7sZHpY24a3EOkigbnZDgQw1FT9rdn4U+U2OkeugBwiSORlPEVKTp9iKvaz1lo//EyqIkNoi+9kgb/njJBEhFAn9PiYQhCqITwG4G3rsa6D4ORKRyGehy32cLRSelIyW3EKEuJ0mDCePzADCvU7IiDB1cGR2mmatVz72YgA5TmoVpSsq8BBJ22WG7yY1nvziAW3//JLJmr+SyeUdZdiA6Lg4p6elwuYENnWo06GXgw42LE/qQrWCy2T2ri6+MHJvyGiSMJBZx5YiYVTBMKY0hgpeXJ/fi8YpYIDoFPIksLD43iC9Lubi5m2vmRUZlW5cSHVYRNCIHFsdyq85IeNmjOp8NoheaSrBHtQwGIfdQJRjNyorGzMwo7KntwUs/VLM38ISQYG0oWhz2Jdt291A99EBUGXM+G0QfR0H0MzDnYKacWrPWjI3lHVhelOjvKZEwRI1Fw53LCXzyC6CphAsYMAF0NX0Y+RxTB5rJHmea2zD11JlscqYUy+Q1wOQbgCk3AlNvBiZdB4y/BM3q6Xhyhw3apAVAdPaYXQQyWYPrOtQo18vAgxsXxuvYZo6EeJVQCsQXetxgVCl0YRrvBDtutodvLUviBdo6bsuU4SKj9mEz90D+0sReiPn9vTpIWGmWZKNRnAMBnJhu2Ihgx2SjMz4oaUSbjltJSQgJEsauUxqKFiGcudw8lFA99IB0PHoxnDwB4oxV0Jj6r0sJi8/n4ZJJXIPRz8qa/D0dEqYoiB7OmJoda38HVH7DZThf+wEQe0p2NAkrLvDwbVsEqgxcBvpF8TrkKimATnzYYJTRdRyw6IbcfRavAm63C70uKXqMNu/Pj4SH3v566BREH7UuqwDrO7gVTFenUCmXcLZbfT67nWDcA4Vz6M/3QMbURZ+REQWb08VmoxNCgkj7YW7LrOAN44aijEaXhuqhByirKAL1kbPYMVPShZzusilcnfgtlZ3oNlCsgvgeBdHD2d5XuRd4wOWvAOmz/T0j4ic8oQRHJEWoMUkh4LmxPEFLAXTiW4pYIJKpR+4GWsqG3D2KZ4D5+B52fKCRq+tPyGgInSaupBAjzOukjlVDUYebh0kRRuSrKGM3nDWJc9AizoAQDkwxbEEw4/F4uGcJl43+fkkD2vvoZ5uQoMCUxRxoKBpPjYGrnPHsluqhB6aqGO7h87iu77nER3JSTpwKE5Mj4HC52QajhPgaBdHDVc0WYO393Hjp/2NLhJDwZHbyEHfVH9ErjIaQ58aqBC2yFJTZS/yYjd56AHDah9y9b9+X7La8tQ8WOzWyJaOjtrZxA2UCIKKGgaPB3O990F/K5RrKQic8HlsPnVFk3AmpM7hLB8zNica0dA2sDhf+uZkrLUYICXC9dYDNAAhlQHQOwl2lI4HdUj30wFQdtQAOnhjR5jrEmOg8c65s9M/Kmv09FRKGKIgejrqrgY9uBNxOoOgaYO69/p4R8ZMWrRkvVyshTS2EwO3A6sRepMkpgE78JDoXkKgBhwXoODbk7taGw1Dw7GwmwtGWPp9MkYQula2VG1Apl1Er1cpRY5RCLnBiRQKtFCFAnaQA7aJkiN02TDZuRbBno//2/LyT2eiNPSZ/T4kQMpS2/lIucQUAX4CwxuPjuDOOHVI99MBkEypRGzWXHecx2ejkNCuLkyDg83CwSYfqTnoQRHyLguhhhm83AO9fA1i0QPI0YOVzYd+ZPFxVtetx+b92osMqgEPfjUnmfUiSDZ39S4jXMI2eBrLRmWbHHixfTBZxF04Hm7RwuWi5Ixk51UAmOgXRR+2DJi4LfWWCjm0ETAhzrblXtZQdTjJsg9gV3GVQZmdHY35uDOxON/6+4bi/p0MIGQyTnNFVxY3DvKEoQxyXCQvEVA89wFXFcCu48jqppMtPxSglWDgulh1/tp+y0YlvURA9jDCh8sz9j3EXEaok4Jp3AZHU39MifrCnphtX/GsnWnUWxEqcaHvnf6F0UyYCCQCJxYBADJi6gd7aIXePF5ghEwmgtzgoE4GMWF40H2KXCeAJADW3RJSMTJ+dj2/aItnx1Sk9/p4OCSAnpEXoEcZB6jajyLgDwW4gG/2zsiac6ND7ezqEkHPpqOBWYMtjACVXCzycSdK4BwkzqB56QKvRzIONL0OEtQXxhqFX6Iab1ZN/LOlCiVTEl4Q+/dOIXz24QIyI9t2AUMoF0FVcLTQSXj4qbcSDnx1ms6empmtwobwOt/Z1+ntahHCYzycmS6i5lMtGj8oadHc+D5iQrMbeul6UNWqRG6/y2VRJ6Fia1b+0OyIFEIj8PZ2g9mVbJCwuPsYpLZgcQWUuyI/cPD72KpfgAu37bIPRMuV8OHliBKtJqZE4f3w8vj/WjmfWV+HF6/tXUhFCAkt7fymXhIm0AhuANI1rrJribMb+qpFn8bZ0USlFb3IIZKiJWoD8rnVsSZd2VSGCTXl5+Zi9l9VqhUQiOfnrGIcbchEPzVoz3v1+Nwrjfvy9ocTExCAtLW3M5kbCCwXRw4TK2oJHF/V/sCx/Gkie4u8pER9zutx44ttyvLqdy+69eGICnrlqEj79aOhsX0J8iik11byPawJl7AQU3HK9cylKicS++l52ZUWrzozECGoK6Wv33HMPXnjhhZO/zs7OxokTgzdC+uGHH3D77bejs7MTf/jDH/Cb3/wG/rI0q/9yiEq5jNqH/aVcrk7uoVgFOUOFfCpm6dchwtmDicbdOKBcgGDPRl9f3o5vD7fhcJMOE1Mi/D0lQsipTD1AHxMo5gFxwReEHGsdOhPbC4vxl2dewh/bq0f9nmabYwxmRs6mMmYZF0Tv/B7bMn4FNy84wnd9PVyC3po1a8bwXZmLytMzzqMvugfKovPx6+c/QPfa5z1+J5lcjorycgqkkxEJjv+FZHQsOmT2bgefx0NX2nLETB7LDzMSDPQWO+55vwybK7kT2r1LctkXn0njJSTQyCKBmHFAVyXQtBfIu3jQ3ZUSIfISVChv1bPB9BVFFET3tdLSUnzzzTeYM2cO+2uBYPCmXUzgfNWqVfjtb3+La6+9Ftdccw0mT56MxYsXw+dcTizOoCD6WDisk+Fwnxwingurk3r9PR0SgFw8AUqV52GJ7hNMM2zGIcUcuIIkKHA2zLnnkuIkfH6gBU99X4m3fj7D31MihJwtC51Z2ShRItzV29XgKxXgO2246xfXj+ph9+atO1G2bQOsDudYTpGcok4zByZhJJT2LmT07kZt1DwEA7OBW6Ww/LYHkVc0+lVa5SU/YO1bz53xfl0WHn7oACKKl+G6ixdB5EGx6vaGarz75H3o6uqiIDoZkeC9aiWecTmAY59D6LZiX4sT/OX3IMbfcyI+Vd7ahzvf3Y/aLiMkQj6evqoYK4qS/D0tQgaXMp0LorcfBTIXAmLFoLtPTdOwQfTqTiN6TTZo5MFbIiDYOBwOHD16FAsWLIBS6dkN6rvvvoukpCQ89NBD4PF4ePjhh/Haa6+dM4jOLOFkXgP6+sZuCbFcV4kIKQ8OnhhCqpU6Km83RrPbixN0iBLTTTU5u6OKGZip/x4qpxYFpn04qpgZtEvMmc+lZYkCfHUQ+KGqE299twsTh7Gk/FS0vJyQMcY0Y2SuIxnxE/w9m4DQKkpht2pnL1Iz0kf1XsrIsSvVQc7OxRehIvYiTGl9H4UdXwVNEH1AdFI6UnJHvwKECXyf7f2S3W4c2l2PXpMdRmUKJiTTajDifRRED3XVmwB9KxscuOLjbnx6OwWWwq3++UOfH4HV4UJShBQv3TCVLX1BSMBjmjsyDZD1LUDLfiBj/qC7RyslyIiWo67bhLIGLc7Lj/PZVMPd4cOH4XK5MGnSJDQ3N2PhwoV4+eWXBw0GHTx4kA2YMwF0xowZM/D73//+nPs/8cQTePTRR70yf3XnfnarlyRAw6N+6yPVaxPgy1bu/PKz1G5/T4cEMCdPhH3KRVjY9yWm6zfimHw6Wy89OJeYc8vLo5bdDtWUFfj9ByVoe+vXZyw59wQtLydkjOmaAGsfIJAA0Tn+nk1gBdHtTONvrvwaCWxH4lexQfSsnq2Q2rWwiOhefgBzH1GYFIHtJ7pwtKWPgujEJyiIHso6K7ngE7MUKHIe6rSf+3tGxEfMNice/uIIPt7XxP56UV4snr1qEjQKeohCggQTXGWy0cu/AFrKgNRZQzZ8ZBrlMkH0Y619mJUVBbmYTnG+cOzYMeTl5bE10ZlMyl//+te49dZb8d13353zGCaTfPz48Sd/rVar0dLScs79H3jggdNqpjPHp6amjsn8xUauqZZenAjNmLxjePq4WQOri4/xKjOmRFJDUTK4w4o5mKHfAI2zE7nmA6iSTwm6JeanLi/PKJyK71rcQEIOLv/rZ0hXuIb1XrS8nBAv6DzGbWNyqWk48wDTDbSJktmx2s487KYgejDoVuSgXVGAeGM5CjrXoizpWn9PKaAUJKqws7oLbX0WdBmsiFGObDUYIZ4KuAhDsDcnCxgWHVD1LTdOmYk+J3fCJKGvrKEXv/3oIGq6jGBKnv9m2TjcuSiH6p+T4BObB9SouSyijmNAYvGguydHyhCnkqBDb8WhJh1mZXGlJYh3XX/99exrwIsvvojMzEw20M0Ex89GKBRCIvnxIlcqlcJkOnfgldn31P3HUsPk+zHh3o+x7m8ZoNDVyLjcwDv9pVx+ltZNDUXJkOx8CcqUCzBH/x0bTK+STQJ8uBJkLJaYn7q8PDu/EDOkPdhZ3Y0KgxQzJ6ZDKKCVLYT4jcsJdFRwY2ooyirXy2DjS+CyGiF3jl1ZPOJ9R+JXIr6mHIXtX6Es8Rou2YiwmKSprBglTnQa2Gz0heNi/T0lEuL4gdqcrLe3l32VlZV51JyMaUy2a9cuts7q5s2bEdbcLqD8S8BhBVSJQOYCf8+I+IDN4cLT31fi8n/tZAPo8WoJ3vnFTNx9HjUQJUGKCagk92cKMg1GmdqWg+3O47HZ6IyDTVrYncPLBCRjIy4uji3v0traes59oqKi2PP3AL1eD7HYfytl2o1uOPm0UmekfuhSocEsgUroxCWJ1FCUeOaAcj6sPAliHa3IsvRnjAaxyamRUEmFMFgd2N+o9fd0CAlvvXWAwwyI5IBmdLW/Q8XuHq6/kKXxKFuIigSPytgL2PK8sabjiDNW+ns6AacwmUvaqWjtg4Pu/0g4BdFPbU4WGRnJvlQqlcfNyXJzc082JwtrdduBvmau/lvBJQBf4O8ZES870qzDZf/agRc2nWAzAi+ZlITv/2ch5uRQG1kS5Jjsc4EYMHUBvbVD7p4Tq4RaKoTF7mLLuhDvu++++/Dee++d/DXzQJvP5w9abmX69OnsfgOYB+bJybRiKlj9pz8L/crkHsgEw68FTcKTlS/HIcVcdjxDv37IB6WBjsk8n5PN/V8oreuB0erw95QICV/MCkZGbL5PV7kEst09XPN3S8Nhf0+FDJNVqMaJ6EXsuLD9S39PJ+CkRcmhlAhhcbhQ3Wn093RIiBOGW3Myq9XKvgYwy81D7ql7w05uPO5CQBY8jSd6tdpBMxc9wdSSZJSXj75b+MB7lDf8mC05GrVtY5+dV3akHK/u1+G7ahMbPFeKebhtagTmprpRXeHZBVJt7dCBSUL8RigFEoqA5lIuGz0qa9DdmVUXU9I02FLVif31vZiYFEErMbysuLgY//d//4f4+Hg4nU786le/ws9+9jPI5XL2HCuTySASnV6LlFlBdtddd2HDhg3suf6vf/0rLrjgAr99D2TkulxKbO7kEh5uoIaiZJj2KxdhsmEbEu0NSLNWoUGah2CWF6/CgUYt2vus2FPbQ02uCfEHpx3oquLGVMrlZD30Pb1cJrq14RBQnOjvKZFhOhq3Evld3yO/8ztszbwXTj7V/h7AZxuMqtnz7pEWHfISBk/EJSRkgui+aE72xBNP4NFHH0VIspmAiq+5cUIxEFeAYNCl454Wbt60CVtLDozqvZwGptM4sGbNmjGZG/tej3+EsdShHf3T0ZZuPeQFC/FIiQtCJVdH2HjsBzRtehX3GEcWrDdRxhQJVMnTgOZ9XCa6oQNQDh6UGN9/EdVncaCqXY/8xLPX5SZjg/m8ZVaRXX755RAIBOyvH3/8cfb3ioqK8Pe//x2XXnrpaccw5/hnn30WF198MZRKJbvy7M033/TTd0BG4wfbOLjBw/xoPTIVNn9PhwQZk0CFw4pZmGzchun6DUEfRGeSeubnxOKT/U3sKsGJyRGIVVGggxCf6j4BuOyANAJQJ/l7NgFTD13vEEDkssLWQQlUwagxcjr6xPFQ29qR3f0DqmLP9/eUAsr4RO7+r6nXDK3Jhkg5lWkkYRBE90VzsgceeOC0xqPMew+25DxoMEtgK78GbAZAHg3kLEWw0Ju5m+65hSkomji6bIG9ZUew5SBw/5WzcNWS/lrKI/Tqt6X415d7sPTSK1FUOPoHEqWHyrH1q4+hM/24EmIktncr8UTfeYhdxZVqUQrdmBTlQHzabODC2cN+v5KNX2P7f1+H1U5BdBKgmBU1zHLcznKgcTdQsGrQ3UUCPianRbIN3vbW97LZCAOrlYh3MA+omddP1dXVnfMYpiE4k31eUVGB+fPns8F0Elx4Qgl22HNONhQlZCRKlYtRZNyJNNsJJFrr0CrJQDBL1siQG6fE8Q4DNld24MqpKXQOIsQvpVzGUwPGn9RDT7A34wTTP40EHTdPgGNxyzGr6XVMbP+cgug/oZaJkB4lR32PCUda+jCPytqScAiiD9ac7FxB9OE2J2MC7qcG3UMGU+qgpwbgCbg66ILTl84Hgwi5GInRo8sYVcm5f9uUWBWmjBtdfd2kEm4ZYFRMLFLSR9+Q5ngzV2pmpA5oZfjb8QTs6OGWJ7lsZmQp7Fg+bzKE/JHX+qs8tG9U8yLEJ1JnckH0jnKPmiUXpUSgtL4XPUYbWxsvJ44CtIGIeVDOvEhwUkw4D0ZIkCqz4rzYECuPR3zGINTgmHwaJpr2sLXRv5D8EsFufm4M6rqNaNVZUNGmRwGtiCLEN+wWoKeaG8f/uFo93A3UQ0+0N/l7KmQUjsRfihlNbyJNtxdRJlpR8FMTUyLYIPrRZh1mZUaxvUoIGWsB9VNFzclGyNgF1GzhxtlLhix1QIIHU+d8U6cK15Rk4dI9uWwAXcxzYQ7vGJr/fQvSRYZRBdAJCRqqBEDDZCe6gcaS/9/eXcBHcbR/AP+dS9w9gZAQggX3FpeiNaC0lBp1p+Wtt2+9fSv/ulKnRqkARYq3aCA4xIm75yLnt//PzBEKLSSBXHL2fD+f5fbI7WZmk+zsPjvzDNqikEqQFOnD1/fn1UBw8gnrCHE0FkEE76HWND23xFRBQp39SAekeE2EBSLE6lMRZCiGs/NSyjCsuz9f35FVBb3RbO8iEeIeqjIA1tPaI8i6EJgsf+dDDzM6//nVnTUow5DjfwlfTyr9yd7FcTjdAz3gpbROMJpR3mDv4hAXJXbEycm2bNmCjRs38qHeZ05OZjQa/7UNm5xs165dfHIy9nW3m5zMYrbmQRfMgF8sED7Q3iUiNqAxivFtoT+m7OqJmw92x95aT0hFAq4Or8GWSzIwQ5wCS3O9vYtJSNeKGmF9LTsKBdrOvTwgyhdSsQgVDXoU1Jw/zRch5MKlIxIy/wioocfcCNtPnE3cS500CJmqAXx9WOMmuIKBUX7wU8ugNZqxJ4fSHRHSpalcgqkXeotjGms+dG+pCQGmv0fwE+d0JGwuf+1dsRYqdCxNrCtOMJoU6cvX2STf1ImKuHwQnU1GNn/+fD452YIFCzBt2jS89957pycnW7t27b+2OXNyspCQEGRkZPBAvNvI3wU0lgFSJZBwGeV9c/JZ03dUeeL+o1EYur03nkiNRHaTEl5SM27vVoEdl6bj9X5FiFL9+2ESIW7BN8baI91iQoI4v82Pq+VS9I1o6Y1OQT5CbGmnxTqHyaXyTHhIKb8q6bh9Xtb5fOK1R+FnLIezk4hFGJdgHR16tKgelQ0U7CCu4b777uN5/luWuDjr3Bh2Z2gC6gqs60Edn8/KVew+lcplpH8TxGxEJ3FqBT7DUK3qBrmlGWMkR+xdHIfTJ9ybd6KqajSgpE5n7+IQF+RwOdFpcrILoCkBCk6lsomfCiisubKJ82g2ibCzxgubK7x52pYqw9+57OM9dJgfWcMXLwpQEGJ9SMh6o6f+hp7iAni0Y+qHQdG+OFpUh+I6LUrqtAj3VXVFSQlxaQfr1MhHMASzERNkGQB62rtIxAVUy8JxUtkXPXTHMbRxCzb6XQtnF+2vpklGictJSUnhndtGjRrF30skEjiEKjaflQB4hlonpSfcrmprbGRMQCPa7oJCHJ5IhMNh8zAx53+YJtkHMTUpZ1HKJOgV6sUnF2W90dlk34S4dBD9Yrnd5GRmozWNC7tQYMPVgulpuzMQSeUokETgnZPB2F/rgX21HtBb/h4Q4iM1YU5YHa6OqEU/by0NLCDknwJ7Aio/KLS1WDxIjtp25KVlE7qdKNFgX24NLh/oZnNmENIJluUF8temE9vhM1pr7+IQF5LsNYkH0RObD2Cv1zS4gjMnGT1SVM9TjRHirEwmE06cOIFLL73U8TquVaZbX4N62bskDkNnFiGlzpoPfRQF0V1GavBMjCr4GCGmWlyZ6DIhPZtJivLlQfSTVY3Q6IzwVraj5xUh7UR/cc4qZxugrQHknkDcFHuXhvwDS7/VbBajzihBlUGKSr0MOf6jEfXAZKyQyIDsvz8bqTJgUpAGk4M1GOrXBLmYhtkRcl4iMRA1HMjcgIdGyvGMYGpzk6Hd/JFWquGztZfWaxHmQz0SCLlY+c1ybCi3pknS7P8NGH1qrgJCbKBcHoN8RU/E6DMxuHErfoV1ck5nxh7mjokLxLaMSuw+WYXYQA94q+iGnjinY8eOwWKxYMCAASguLsbYsWPxySefIDo6+l+f1ev1fGnB5jjrmlQuFERvwQLoBosYoQoDYtWUUspRNGg0KC0t7dA+dnpehsl1P+CR0Qq8S7m/zxLoqUCknwpFtVocK6rH6Dhr5w9CbIGC6M6oJhcoOWhdT5gByJT2LpHbBsk1JgmfBJS/miRoMFpf2WIW/tGNXKYC+x8PSyPGh5swzK8Jw/0a0dNTTz3OCbkQIX2hzdiGKB89RuAQmjCx1Y/7qP7ujb43pwZXUG90Qi7a5/mBsECEeBQjv4r1aaMgOrGtfV6TeRC9b1My/KTj4Qr6Rfggs7yRpxbbnF6OKwZEUFoX4pRSU1ORkJCAd999l89N9uCDD+K2227Dhg0b/vVZlqL12Wef7ZqCVbHUYoJ17hxK5fKvVC6sFzqdcuxPZzSfTol0KD2nQ/v6WabHsdEiDAmXIFZ/AsA4G5XSNbBRXyyIfry4HsO7+0MqcajpIIkToyC6szHqgIx11vXwQYC/G6Ww6WIGkRzy0HhUy8OQUqs+FTBvJUj+DyII8JRaECg3IUhhRHluBnb/8AHevK4/7k1yjZtCQuxCLEW6JQYDJZmYIWzHCmGJtYd6O3qjF9Q0U250Qi5SpV6KH4usPYPHiFOx2d4FIi6pSN4DJfJuCDfk4XL/HCyH82MB84mJwfg2uQCFNVqcKNWgb7h1RAchzuS6667jS4sPPviAp1Rlvcy9vb3P+uxjjz2GJUuWnH7PPhMVFdXJqVwoxemZdp8Koo/2b7R3UQiLL5isQfSkHsEYNXRgh/e3oSAdVwYX4grxNmzB3TYooevozkZ9KaXQ6ExIK21Av0hqc4ltUBDd2WRvBAwNPCcwYulpoy1U6SU4plEjs1GJ3CY5cpoVyGlSoCqwP8JusGZeya4515bWILm31GxdZGe/sq9Jzoizb0svh6m+nPdGJ4R0TJYlCrHGDEQoyxFbuxM5/pe2uzd6ci71RifkYnySFwSdRYwkn2b0aOjYMGRCzkskwj6vSbi8ehmm++XBX+UaV05+ajlGxgZgZ3YVdmRVoVuABzwVdCtGnFtwcDBP78JSU/wziK5QKPjS6QyNQF2hdT0oofO/n5OoN0pwVGPtNDI6gILojsRDKUNYwNl/LxdjzbGemBlQgP7SfGTUpaDId4hNyucKxCIR743+V1YVDhTUok+4N8Q0CyuxAbpycyYVaUBFKu/jjF6zAInc3iVyOhYBSG1QYk+NJ1JqPXBco0Kx7vzH0dRYA18FeE4tFhz34gFzC3xkLEhuPitITgjpOkbI8GGKAY+OUWBo0ZfI8buEB15aQ73RCenYA+dvCgL4+gM9ylFyxN4lIq4sV9EbFbIIBBuLcd9wOQ7ANQyM8kVWRQPKNXpsSSvH7KRwSutCnMrSpUsxcOBAXHvttfz9nj17IBaLO6+HeXtUZp5K5RIGKCmVS4s9NR4QIEIPDx1ClW3PIUScT6VJjU8PGnH3UDlGFn6Cn3wGt3k/5E76Rvhgf14t6rVGZFY0oFdoxx9cEEKJgZyFvhHI+sO6Hj0S8A63d4mcRo1BghVFfrjjUAwGbeuNmXt64sWMcPxR4cMD6CztSqyHDjND63Bfj3K83a8Aq0dkYVHVhyh+fxH6aPZiaogGI/2b0Ndbh2i1gQfRKYBOiH29tdcAA6QIbziGqPqUNj/f0hudYb3RCSHt9+mpXuj9vZsxLrDB3sUhbtIbnblvmBwquMaEeKwX3KTEEEhEIuRVN+NYcb29i0TIBUlKSsKTTz6JLVu2YOPGjbjjjjuwaNEiqNVq+xXqdCoXmlD0TJTKxT28tEMPgyBBpOYQouv32bs4DkUmEWNAtPXBGgumCzQBK7EB6onuDNgfe+Y6wKQDPEOAmNH2LpHDaxYpeY+59eXeSK71PCt/uYfEjOH+bFLPJj4kvY+3Fl5Sy7/2sVowdHGpCSEXorxJwJ8YjsnYheGFy1DoO7TNbYad0RudTfAWQb3RiZvJKqlBUGbxBW1Tb5bjy8LefH2mKhWHsiqRW1bbSSUkxCpb2R+Feg9EqZpwmegAajEPriDQU4HRcQF8iDlbOqMdKigoQFVVlc32xyaQjI6Ottn+iPNauHAhTpw4gauuugoSiYS/f+mll+xWHqlZC9QXWN9QEP0su2r+nlSUuK6SBgEbDIMxW7EPl+S9je+SvoEgkti7WA4jKdIHB/JrUdNkwMnKJijtXSDi9CiI7gQCm7MATQ7AToYsjYuYTorne9bQIA9A4Oz/4GOPUTCn/f3r3dtLi6nB9bgksBH9vJshozEYhLiEtaLxGI99iNIcRET9IRT7tD5Jj7dKht5h3jheosGek9W4ahDlRifuoaq+ib/e+8EmAGxpP9+xN8BnhBT60izc/up/z/paRZ11v4TYmiAS46fqeCwJP4w58mR8Y9bBLHGN21+WpzW/uhn5Nc3YcKIMY/xsG0DvlZgIbXOzzfapUquRnpZGgXTCvfzyy3xxBL66UwF0nsqFJg5sUaaT4mSTEmIIfDQ1cW0/GsZgijoVwU1Z6F3xO06EzLF3kRyGQirhgXTWE31/Xg3GUMYn0kEURHdwPfxEiGg4laYgdizgEWjvIjkcowU4oVHhcL0a9X4h8PAD2LzXfb2bMTu0DtNCNDwFCyHE9dSKfJEaNAv9y3/B8KLP8IvPe21uM6y7P9LKGnhPdBbAoIaQuIMGrbUdnHT5XPTvk9ju7fSCFOvNSbxdHRchIPyJp/n/pxxNw19rfkJ9s2uk2SCO6c/6CFypPohuvk3oX/4rDoUvgCtgedAn9w7Bt8kFqGo04IQNew2yHugsgH7dI68hJLpHh/dXXnAS3766lO+XgujE0fjp8q0r1Av9LLtOpXLp563laUiJa2sQ1EiOWoyxeW9hdP4HyAyYBKPUw97FchgDo/xwqKAOFQ16lCspJy/pGIodODLBjK8uV0EimAGfaCCi7VQF7kRrFuFIvZovLE8rI7YYUX/4D9yVoMHzU/vau4iEkC6wP/IG9KlYhZi6ZIQ2HEOZV79WP++llKF/pA+/mNp9shqXUI8E4kb8A4MQGRNzQTfi5joJguRGDI30gUhk7emXVWy7VBGEnI8ZYry8U4+PZ6r4JNLHQq6AyUV6o3sopJjUOxhrjpQiq0ECZbfWR1JdKBZAj4zvY9N9EuJIQj1F8DSUW99QEP0slMrF/RwOm4f+ZT/DT1fIJxn9q/uD9i6Sw1DJJegX4YNDhXXI0FBWB9IxFER34ByEot3vYnS0FGaRDJJeMxxupuW0gkqb7KekSnNBnzdYRDhQp8ahOjWMgjV47iM1YZBvMypP7MbaTR/B4DscBzP9urRchBD70CjDkRY0A30rVmN44WdY1futNrcZGuOPE8UaVDboUSyn/E6EnEujScxHeTFsLhEHuwwhbuKLQ0Y8Nz0UIcYaJJX9hAMR18NVxAZ6on+ED44W1yNw5kOoaqYeo4S015WJUvBmySucUrn8I8Xpjiovvj6GguhuwyKWYXvsQ7gi9QEMLPkBGYFTUO5FD1JbDIrxw9GielTpxVBEtd7hipDWUBDdxgH0xMReaG7WdnhfSSFi7LvVA5CIkKXoh14OdGFQWlrKXxe+tMKm+9UaTK1+3SwAxzUqJNd4Qnuq5znrGTfErwlxHnqIRcDaxnr+///7KZkvXVEuQoj97Yu8Cb0r1iK2dhdCGlJR7mWdBLG1HgmDon2xN7cGJ+olNNcEIeewt8YTJkGEMKUBsWpK20Lsl7bvB/0luF/1O4YWfYWjIVe61DD1S+IDUVBRizoPX7y2uxZjR5h5DldCSOvm9ZZZV6gX+llSG5SoNMigklj4fTJxH3l+o5EWOBWJVX9gcvYLfJJRi5hCfoynQoo+4d78obXv2EUQ2NMmQi4C/UXZEOuBzgLoyx+fh8TooIvej0gwo1fVOshNdfg1zQh1v2A40qVBXV0df50xbyES4mM7vL9tf+3GoR2boTedv/dNfrMc26u8UGe0/sr6ykwY5d/Ig+dn9owz6HX8dcS0ORg1OKnTy0UIcQz1qkikB01F78p1GF64DKt7v9nmNgOj/XCkqB6NRjM8+03qknIS4iyq9BJ+I97Sk416oRN72mbsj0V+R+GnK8DA0h+xL+pmuAqpRIwRQSasPalHVo0XnluTihevoF5yhLRGqqvGJTGnHjYFJdi7OA7lz1O90Nm9skJMgUJ382fsQ+hWtxdBzdkYUfgJdsfcZe8iOQw2L9aJkjooIxKxv0SPwYPtXSLijCiI3glYAH1Qz4iL38HJbYCpDlpBjtt+b8Dy/o555xoQHHxBeVXPx9M3rdWh5H9VeSGryXojr5aYMdyvCX28tayT/nl5+wd2uGytlYsQ4niSo25Gr8oN6FG7AyENJ9ocwiiXijG0mx/+yqqCz+gF0JvoRoOQFjtrvCBAhDgPHcKVRnsXh7g5C8TYG7UYl2U9jcHFy3EkbC70UmugyBV4SIGq319H6Nxn+WSjA6J8MXdIlL2LRYjD8iv9C2KRCI2yQHg60IhtR8A6njHjAhvsXRRiB1qZH7b0eBQzMx7DsKIvUeQzGAW+w+1dLIeZiyTOy8Lzon97rAG3zhAgYekMCLkAlAjW0dQVAkXWNCTJ5r6oanbPoA4bXXO4XoVvCgJ4AF0EAQN9mnBDdDX6+7QeQCeEuKc6VQzSgqfz9VEFH7VrGzbJjEoiQOoViLVZNOSVEKagWY78ZgXEEHhPNkIcQUbQFFSrukNpbsCgku/ganQ5BzC/j3UywCd/O47jxdYUhYSQf/OqTOGvdcqOd+hyJRqjGAfrrOmuxlIQ3W1lBU7C0ZAreAxlWubT8DDQZPAtenqbYdY2oFBjwsoDhfYuDnFCFER3JCY9kPG7dT20P4qFYLijeqMYP5f44c8qbxgEMUIVBiyIrMGlgY2Q05A0QkgrWE9Fs0jChzGGaw63axh9Hx9ryqZf0htR3Uh5n4l7Yw+xd1ZbA3n9fLTwk1NKM+IYBJEEe6Jv5+sDS76H0mhNL+hKru7tifEJQdCbLLj16xSUa6xpCgkhZ8sZ+hzGf9WEGlV3exfFoew+NZcJm8ckWm2wd3GIHW3vvgRV6h7wMNZgdtoSSM0dn7fPFcjFQP2eH/n6a39kolFP89+RC0NBdEdyciugq7fOLt5jItwNC4+ziUO/LQxAsU4OmciCcYEazIuoRZCCTm6EkLZplBE4ETyHr4/K/9AaEWxDtIcF+rJsNBsFvL0lqwtKSYjjSm+0TkgmF1sw3I96oRPHkhUwHhUePaEwN2FY0RdwNSw9xVvXDERcsCdK63VY/FUKmmmCe0L+TSTB9jwzTBKVvUvikPnQL6Ve6G7PLFFida/XoZX6ILQxjfdIZ3PvEaDhwO8I85SgqlGPD7Zl27s4xMlQEN1RVGUBZUes6wkzAKkC7kSs8kZx4AhsqfSGURAjXGnAdVE1SPLR0mRmhJALkhx1E0wiGaI0BxFVv7/Nz7NzTO22z/g6y0WbXUGBQ+Ke9GbR6V7oQ3ybeKojQhyKSIydMXfz1aTSn+CtK4Gr8VHJ8PkNQ+HvIcex4no88MNhWCz0t0gIaR3rN9ISRKdULoSpV0VideIb/L4ovmY7pmQ9T4F0xmLCDUnefHXZzlzkV1NKT9J+NLGoIzA0A5nrreuRwwDfaLgTnToYYTe9gyZVICQia/7VAT7NoDkeCCEXo1ERimOhV2Jg6Y88N/qPPkOtkfJW6AuOYUi4AikleryyPg3LbhjaZeUlxJGGgTebJfCVmTDQt9nexSHknPJ9R6LAZwii61P4OX5Dz+fgaqID1Ph00WAs+DQZG1PL8eqGdDw2PdHexSKEOLDsJgVKdHIoxBaMpPlMyCkl3klY3/MFzMh4HL0r1/Lx/xvjn4IgOncosLauDqWlpR36nnV1jp9ubWi4AmPiArEzuwrPrD6BL24cCtFF9N4sKChAVZVtcs4HBgYiOtq9YoHOiILojvDIOGsDYGwG1IFA90vhTlXfX+eBipipkIrEkBsbcHV3A6VuIYR02L7IG9G3/DeENxxD99pdyPUf0+Y2i/p741BZFTanVWD3ySqM6hHYJWUlxBGU6aQ4qrEOi58QqIGUHmQTRyUSYWfMvbj26A3oVbkBB8KvQ6VnAlzN4Bh/vHZ1f9z/w2F8/FcOIv1UuH5kN3sXixDioLaf6oU+3K8JShpJRs6QHTgB6/ACpmc8id6V66A21mBtwsswSK2jDxmt1pozfdvWrfhrX9vzSrXGUJHLX5ubHbdDBguYPzunDy57awe2Z1Riw/EyXNYv7IID6L0SE6G1UT1VajXS09IokO7gKIhub+XHgKpMPjwVvWYCYvf4kejMImwo90G+VgGIgMZjWzDQrwlBvYbbu2iEEBfQLA/EkbB5GFL8DUbnf4A8v5F8UrrWRHpLcd3waHy9Jx8vrk3DmnvGQExDYogbYJkitlayYa0i9PLUIkpttHeRCGlVuVdvZARORkLVJozJfw+/9nkXrmjOgAjkVTXj/zZn4unVJ+CrlmNWUri9i0UIcUCUyoW0JitwEtaIZJie+SS61e3FNUdvwrqEl1DlEc+/rtfr+euQhAgMHdi3Q99r724jdmQBeoNjT27bI8gTt4+Nxbtbs/HsmlSMiQ+El1LW7u1ZD3QWQL/ukdcQEt2jQ2UpLziJb19dyvdJQXTH5h4RW0elrQOyN1vXu10CeIXCHVTpJfi9zBf1JimkIgHeRbuQv+7/IL72FnsXjRDiQvZHLELfsl8R1JzFeyumBc9oc5v7J8bj14PFOFGiwcoDRZg3NKpLykqIPR2tV/HJRNkQ8DEBNAScOIdd0XchrnobDwZE1e1Doe8wuKL7JsahslGH5XsLsGTFYZ4z/dKeQfYuFiHEgTSbRNhX48HXxwVREJ2cW07AWPzYbxnmpC1BgDYPC47ciJ3d7sbhsPmnP+OlViAswJov/GJ5q+RwFnePj8PqIyXIr27GS+vS8fKV/S54HyyAHhnfp1PKRxwPTSxqL4IFSP8dMBsAn0ggyj16YGc3KrCi2J8H0L2lJsyLqIFn/Ul7F4sQ4oJ0Ml/sj7yRr7O8uRKLtYdFawI8FbhvorVHBstBW6+lHrnEtTWaxNhTYx3OO9q/ER5Si72LREi7J0w7GnoVX78k713rtbUL4kPOZ/fFzP5hMJoF3P7NARwsqLV3sQghDoS14wZBjEiVAbHqtq93ifti6c++TfoGOX5jIBUMGJf7f1hw5Cb0lJXBHSllErx6VX++/v2+AuzMsk1+c+K6KIhuLwV7AU0RIJFb07iwdC4unv98T40H1pb7wiiIEaXS45rIGsp/TgjpVIfC5qNBHgxvfRkGlK5o1zY3jOqGHkEeqG4y4K3NmZ1eRkLs2TazNC7sxjtUYUBfb2s+TEKcRXLULdBLPBDSlM7zvLoqiViEN+cNwCXxgdAazbjpi/1IL9PYu1iEEAfLhz4uUMOmjSCkVVq5P1YlvonNPR6DTuKJkKY0vBnwE36Zp0KMzP0e0o6IDcCikTF8/ZGfj1InKtIq147cOqqGMiB/p3U9bjKg9IUr01tE+L3MB/tqrT3dBvo04fKwOqhowhNCSCczS5TYHX0HXx9W9CUUpraDDnKpGP+dbR2Sx/KjZ5bTsFjimk40qJDbrIAEAiYGNdCNN3E6WpkfkiOt6QDH5L0Lucl10xGxtumjhYMxIMqX3+Bf+2kyMsqofSLE3bEH4pv5vCbAeMqHTtpLJMKx0Cvx1aCVOBE8g8+Pc0WiDG+HrcX0mq/hZyyHO3lkWi9E+6tRXKfFE78eg8D+sAg5BwqidzWzEUhfYx1yGpgAhHRs0gZnGCa+stgPOc1KSEQCpgTX49LARtBcfYSQrpIWPB2V6jgoTRoeSG+PS+KDMKV3CMwWAf9dfYIupIjLqTNK8FeV9eH2qIBGBNLIMOKkDoVfgxplNDyMNRhe+BlcmYdCiq9uGoa+Ed6oaTLg2k/30oNeQtzccY0KpTo51BIzRtO8JuQCNcsDsDH+v7i76jqsOGHkcZoE7SHcUPEqLqv5Gv5G90jzwtrXt64ZwEd+/X60lM+NRci5UBC9q+VsA5qrAbkn0HMafwLoqmoMEp7/vMog44361eE1SPTS2btYhBA3I4gk2BlzD18fUPIjvPTtuxh8amZvKKRi7D5ZjfXH3eMCkrgH1ttoY7k3T68WqTRgoE+zvYtEyEWziGX4M3YJXx9Y+j38mvPgynzUMiy/ZTj6hHvztGMskJ5FgXRC3NbGCmsv9LGBDVDSSG9ykQrN/pi/Uov7SmYgW9kXIgjopT2ERRX/w4yaLxFgLIGrGxTthwcnWefGenrVCRrtRc5Jeu7/Jp2iJgcoOWhdT5gByFRwVSU6GdaU+kJnEcNPZsLlYbXwlrnmhE+EEMeX5zcKhd6DEaU5gDF572F9wgttbhPlr8btY3vgnS1ZeOH3VIxLCIJaTs0mcX4pdWqU6uWQiy2YHFzvys/zHVKDRoPS0tIO7UPTQDd2Z8rzG80nSYut3YmxuW/it95vu3RHFV+1HN8uHs5TuqSWarDg02T8cNsIxAVbR5d0hrS0NJvsJzAwENHR0TbZFyHk7yD6lGCaJ4F0XJ7RD2sCJiHIUIzhDRsRrzuKntojfMlS9sce72moloXBVd05Lg57cqqxK7sadyw/gFX3jIa3UmbvYhEHQtGArmJsBjLWWtcjBgP+3eGqcpoUWFfuA7Mg4hOVzab854QQexOJ8Ff3B3DtkUXoVfUHjoTNRYl3Upub3Tm2B34+UMTz4729OQuPTU/skuIS0lkaJT5Iq7EG2sYFNtAD7i6kM5r5a0pKCg6l53RoX4aKXP5qMlEanhbbuy9BdF0yutftQffancj1vwSurCWQvuDTvUgva+CvLJBua42N1vQQCxcutMn+1GoV0tLSKZBOiA3kNcmR0ajiaVMnBNHDVWI7lfII/B5wEwKNJdZguvYoD6j30B3HEY/RPJiuF6vhalg6l3euGYhZ7+5EblUTlvx4GB9fP4T/PyEMBdG7Asulm7kBMDQB6kCg+zi4ck62rZVeECBCd7Uel4XUQUZJgwghDqDCsxeOh8xGv/JVGJfzBr5L+hIQtX6CUskleG5OH9zyVQqW7czFnAER6B1u7fFDiLMRq7yR5TUIFogQ56FDL09KsdaVDCZrED2pRzBGDR3YoX1t3dqEA1mA2WLdJwHqVVE4GH4thhV/hXG5b6LAZyifXNqV+XnI8d2tI3hKFx5I/2QvnhjlZdPvodNZzxPP3zwZ04f17NC+0goqsfClFaiqqqIgOiE2sOnUhKIj/BrhI6P2gNhelSwca/1vRICxFCM16xGvO4aBTTsQrz2CLb5zkaNyvTn+AjwV+Oj6wbj6oz3YnFaBl9el4cmZve1dLOIgKIjeFcqPAVWZ1mBNr5mAROaSzwmSaz2QXGvt3dbHS4sJQRqaQJQQ4lB2R9+JnlWbEdKUht4VvyM1ZHabQ9X92M1JpBJ7i3S4f/levDwxAOI20gTQcHXiaFjgPHDWwzBIVPCRmTApSOPK2S4cmodShrCAjj2MUyvlNiuPK9kXdTMSK9fDV1eEEYXLsKubdT4MV+bv8Xdql4zyBjy13Qh5SA+bf5/uoX4Y1DPC5vslhFy8jeU+/JVSuZDOxlK4/B5wM6J0mZhQ/zP8TRWYU/MZjqmHY5vvlTCLXOu6pH+kL16fm4T7vj/EO1JFB6ixaGQ3exeLOAAKonc2Nolo1ibrerdLAK9Qe5eoUyYo21bpheMN1uE8w/waMcKviW7OCSEOOQP93qjFGJv3Nsbkv4/sgAnQ1FS2OVRd4hmA8MUfIqtGjfGLn0Lj4fWtfh+VWo30tDQKpBOHcVA9AqqgQRALZswMqYeC0qwRF2SUqLE19j+Yk/4whhQvR2bgZFR6JsDVsV5zLJXLDV/sw9GieoQseAlVehEi7V0wQkinqdJL+BwnzGQKopMuUqjsieWKhzBKswGDG7ejX3MyQg2FWBNwE+qlgXAls5PCUVjTjNf+yMAzq0/wNGrs/4h7oyB6Z7KYgLRVgMUI+MYAUcPhaowWYEO5D3KalXwGZ5Zftb+P1t7FIoSQ8zocNh/9y36Fn64Aw4q+wJ7GGP7/M25/Agn9B593u+wGMY7UAqHT7sLkm2+FSnLuz5UXnMS3ry6l4erEoSYdO+zRn693bzyGQIXrTghFSE7AWGQGTELP6s2YnP0Cvk/6AoLI9W95/E71SJ//3nakVgE7KwT41zQj2t/1ctYSQoAtld48hWo/72aEq4z2Lg5xI6zX+Q6f2chT9MJltd8gyFSCBZVvYZX/LShVuNbcf3eN64HSei2W7y3g+dE95BJMTAyxd7GIHVG26s6Usw1orABkamsalzZy7zobI6T4tcSPB9DZZCbTQ+opgE4IcXgWsQx/dn+Arw8q+Q4homq+HhAeg8j4PuddLh3YCyHeChgFEbKM/uf9XEi07YfRE3KxshsVeOhYFF/XpKxGoKHE3kUipNNti30IOqk3QprSMaj4e7gLL6UMT13qD23OAZgFEVYfLsHJSuvEoIQQ17KpglK5EPv3Sv82+GGUyaKgsjTh6qoP0EN7FK5EJBLhudl9MWdAOEwWAXcuP4hNqeX2LhaxI9eK6joSlgO9+IB1PWEGoLDtJD/2JvEOwmHVYJTq5VCILbgirBZxnnp7F4sQQtol128M8nxHQCKYcIN0Q7u2YXnQJ/YK4amqsioaKTBBHF6FXoobDnRHg0mCEGMJard9bu8iEdIlmuWB+LPb/Xx9VOHH8NEWwl0opCJU/PI8IlQWmAUBa4+VIr2MgmyEuJImkxh/VVvnIpsSXG/v4hA31iTxwU+BdyNb2RdSmDCz5iuM8XKtDhtisYjnR5/eLxQGswV3Lj+ANUdcq46k/SiI3glk5iYgY531TeQwIMC1eiWWacUIXfg6msUe8JSYMTeiBhE0hIwQ4kxEImyLXQqTSI7+khzM79O+of5BXgoMjmZTjQJb0yugM5o7uaDOYdWqVYiNjYVUKsWAAQPOO0nrmWbPns17d7QskyZN6pKyutMN9s0Hu6FYJ0c3tR6T6n+3ppkjxE2kBs9Cgc9QSC16TM5+ERAscBtmE4YFmpAY6gVBAP44UY5jxRRoI8RV7Kj2hMEiRrRKj57UkY3YmUmswO/+NyJVNQRiWLA04gCu7u1aadRkEjHeuWbg6R7p935/CL+kUYcqd+Rav9kOQCICutfuBEw6wCsM6D4WrmTPyWp8kuMFqZcIHpZGzIvRwkvqRjclhBCXUaeKxr7IGzGq8BO8NU2JZ4X2paMa3t0fOZVNqGk24M/MSkzt43oTRl+IkydP4qabbsJHH32EsWPH4t5778XixYuxa9euVrdLSUnBsWPHEBlpnfpOJpN1UYldn8kC3HMkGsc1avjLTPhyUC6W51O6NeJmRCJsjnsc1x9agCjNAT7R6AH42rtUXUYsAib3DoFMKuaTjbIHvwaTBYNjrA+CCSHOa13Z36lc2AhJ4t4aNBqUlpZ2aB+ahoYObS+IJNjotwAWkQR9m5Px7ZUqPF5zHKWlw9BRdXV1cARSiRhvzhsAP7UcX+7Ow/JjDfCfdi8sgr1LRtw6iM56sz344IMoKChA37598f333yMxMbHN3mxr1qw5/X7ixInYvHkz7OHpsQp4GisAiQJInAOIzzPznBNae7QUD/54GAaLCLrC4xjlXwMvaYK9i0UIIRctJfIGdMv/GeGe1bhKWI9jmNauC6hJvYPxU0oR0ssaEB/iidhA65Bad8R6nb/yyiuYN28ef3/nnXdixowZrW5TXFwMQRB4O09si/U6fSotAtuqvKEUW/DZoFx08zDYu1iE2EW9MhLbYh/GlOwXMKrgA2wU3Qh3wkb5jOsZBLlEjJT8WuzMroLWaMboHgH8a4QQ56M1i7C50puvzwx1jOAisY+WEbGsY8qh9JwO7ctQkctfTaaLH7UoiMTY7DsPVSUFGOdbiv/6rcaM76twpMHXJmVrbm6GvUnEIvx3dh90D/TAs2tOwCtpKnZWWHBldzOUMteJ/REnCaI7e282z6pDePJSufVNz2mAynV6u3y5KxfP/p7Kb877eBuw7senILvzXnsXixBCOsQsluML03Q8If8GE7AHFQ0nUO7Vp83twnxUGBjti4MFdbx3X/hwldteOM2cOfOs9xkZGYiPj291m3379sFsNvN2u7a2FrNmzcKHH34IP79z95DU6/V8aaHRUH7fc2Ft9IsZYfi+KABiCHgnqQADfakHOnFvJ4Jno1vtHvSs3oK7Zb/i/9xs0AsLlo+OC4RCJsau7GocyK+F1mDGxF7BPM8rIcS5bK30RrNZgiiVHkk+1Ma7M4PJGkRP6hGMUUMHdmhfW7c24UAWYLZ0LFUlC6Q/n9sXRu9CTO4hxerhR/Cu4jZoRNYHPxdj724jdmQBeoPjdAq5YVQ36GtK8MKWYlRCje/2FWBan1CE+6rsXTTiTjnRz+zNFhISwnuzHTp0qN292Xx9ffni4eEBewjN/IZPPFeligOCW+897yzYsX1lfTr+u8YaQF80MgbXRDcDZsqBTghxDWmWbvj6iIEHHSedfAkioX09MEbGBsBXLUOT3oy/sio7vZzOwGAw4I033sAdd9zR6ufS09ORlJSEtWvXYu/evcjNzcVjjz123s+//PLL8PHxOb1ERUV1QumdG2ujX8kMxbL8IP7+xd7FfJg3IW7vVFoXjTwEoeIavHOZEu5oSIw/JiUGg4XNU0s1fMJRk5lSMhLibNaUWjvqzQytp1QuhPNQyhAW4N2hRa081RnUBoyCGFf/1Ix8vTd80IBbLT8iyk950WXzVtmubLY0JFyJsm8fgYdUQIPOhJUHi7AvtwYWdlFOXJbY0Xqz3XbbbRfdm40Fz6+55hreq+18WE821oPtzMVWcoa9iBf+0qPQZyhcgdFswUM/HcFHf57k75dOTcCzs/vwHIuEEOJKHt6oRyNUCG7K5Hlz24OldZmcGMLX00obkFXRsVyCruCZZ57hbTEbRdYaFjDftGkTD6T369cPr732GlauXNnq5+vr608vhYWFnVB658Wu1d/IDsHHecH8/fOJxVgQVWPvYhHiMPRSb2zo+Rwsggg3D5RjmHAY7qhPuA9m9g/jw9Fzqprw6+Fi6GmCbEKcRqNJjG1VXnydUrkQR6bRA0+XjEaz2AMhxiKMq/8FrshYmYuJoUb0DPHk1+N7cqrx66FiNOio06mrcqggurP3ZrNIVXhqmx6CyKGy5FyUJr0Jt3yVgl8OFvML7deu7o+7x8dR/kRCiEuqbBbwnWg2Xx9R8AkCmq0PD9vChuwNOTVJ29a0CjTqLj6PoLPbunUr3n//fXz33XcXnFYtODgY1dXVZ6VsOZNCoYC3t/dZC7FiF+xvnQzBeznWBzrP9CrG9dHV9i4WIQ6n2GcQVpnH8PVbhBUIaMqGO4oN8sQVAyIgl4pRUqfDTweL+HU/IcTxbarwht4iRqyHDr29dPYuDiGtKjN5Yq3/DbBAhH7NyejVnAJXJBODp3JhnaukYhGKarVYvrcAR4rqeGYH4locNohOvdnsp6pRjwWf7sVfmZVQySRYtmgI5g6hofOEENe2C0OQ4zcGUsGIKVnPtjuty4jYAAR7KaAzWbAprZwHNd0Ne4C9YMECHkTv3bt3m5+fP38+du7cefr9nj17eBo3Fiwn7Wc5lQP97ZPWAPqTCSW4KYYC6IScz6+mS7ElxwQlDJiT9hCURvfsyRnhp8LVgyKhlktQ3WjAipRC1DY7Tq5ZQsi5/Vpi7bgxi1K5ECdRpIhHstcUvj6x7if4Gcvhilhn097h3rh2WDTCfJQwmC3YnlGJlQeKUNNE7asrccggOvVms5/86iZc9eFuHC2qh7+HHN/fNgLje1mHhxNCiMvnze3xOHQSL4Q2pmFI8Tft2oyN1pnaJ5T3PCioaUZ2g0M2rZ1Gq9XydGxz5szBFVdcgcbGRr6wnhcsZZrR+O/hjOyh94MPPsgD6b/99ht/wM3mQSHtZ7IAS49Hns6BzgLoi7tV2btYhDg0C8SYt1KLcgTAR1+CmemPQGxxz17YQV4KzBsSBR+VDBqdCT+lFKFCQz1bCXFU5TopdlZ78vUrw8+fvpYQR8OC6AWKeMgFA2bUfAWJ4LpBZT8POa4eHImxPYMgk4hQUq/Dd8kF2JtTTfOQuAiHu9On3mz2c6yongfQ86ubEeWvwso7RmJAlHXiEkIIcQdNiiBsj32Ir48o+LTdw/3ZQ8cx8YF8/XidBLLAGLiLjRs3IjU1FZ9++im8vLxOL/n5+ejfvz9Pt/ZPjzzyCP/atGnTePD8rrvuwhNPPGGX8jsjnVmEO4/E4OcSf0hEAl7vW0gBdELaqUYr4G3RTdBLPBClOYhxua/DXbEA+tzBkTygrjWa8fPBYhTWNNu7WISQc1hV6svTYgzxbUKM2nWDkMT1CCIx1vstRJPYC0GmUoyv+xWuTCwS8TjawuExiAlQwywISM6twfLkAt5plTg3hwqiU282+2GpW+Z/sgdVjQb0CffGz3eO4jkTCSHE3aQFTcdJv0t4WpepWc+1u5di/wgfdAtQ8xucwFkPw2h2j7wurM1m7fQ/l27duiEvLw+XX375v7Zho8w+++wz3saXlpbi6aefhlTq/POJdIVagwSLDnTHpgofyMUWfDQgH1dHUI80Qi5EsSgU63s+DwEiJJX9jAElP8JdeSikuGpQBCL9VHz4+arDJTRRNiEOhqUK/PlUKhfqhU6cUbPEmwfSWbvbr3kv4rRH4eq8VTLMSQrHZX1D4SGXoF5rxG+HS7DuWCkaaS4Sp+VQQXTqzWYfP6UU4uYv96PZYMbouAD8cNsIBHsp7V0sQgixD5EIW+Ieh07qjZCmNIws/Lidm4kwKTEEHlIBmv2/QupQLSxxBTlNclyZHId9tZ7wlJjx9eBcTA7W2LtYhDilXP9LsDPmbr4+LvcN9KzaBHelkEr4jX6PIA/eY27dsTI+QpUQ4hiKLH7IaFTxh+czQulvkzinQmVP7PeceDo/usrs+g9s2f1hzxAvXD8yhvdOZ1MZZFU04ps9+ThUUAsLm+CIOBWHusWn3mxdix3btzdnYenKozBZBMwZEI4vbhwGL+WF5aEnhBBX0yQP5PnRmaFFXyGqbn+7e/RNCTOi6fhWftFEiK3sqfHAFXvjkNusQITSgJ+HZ2OEPw0JJaQjUiIW4XDo1RBBwLTMpxFTu9veRbIbqUSM6f3C0DfCOl/U1owKnsPVHSfLJsTR7DLG8dfJQRr4yMz2Lg4hF22v91RUSsOgtjRiYt1K6zALN3lYzfKkXzMsCqHe1olH/8qqwg8phSirp/lInAlFm92U0WzBk78ex48phfz9XeN64OEpCRCLKehDCCFMVuBEHK27Av3Lf+XBlW8Gfg+drO15Iug0Smztl8owbGnoDhPEiFfU4bHgA2gqNeBgafv3UVLVOT3WCys1OJhZ3KF9dFbZiOto0Gh4Z5mOqqurO/s/RCJsj30YKlM9Eqo2YXbaUqxKfAMFfiNgD2lpaXbdB8vjOiEhGGqZFPvyangO11CJLyCWwBEVFBSgqsp280EEBgYiOjraZvsjxBZEUjmSjd35+rzIGnsXh5AOMYuk+MPvOiyofBPxuqNI0B5Ehnow3AXL+DBvSCSOF2uw62QVKhv0PCbX3VMCscLD3sUj7UBBdDfE8i/d/e1B/JlZyYM9z83pi4Uj3GcSPEIIaa8/uy9BhOYwArS5mJr1LFYlvsmDLoR0BdYzxX/qPfhDOYm/b0r7C1vWvYXNpoufUExrsE0OxuZG6xDc//2UzBdbsFXZiOvQGa09LlNSUnAoPafD+zNU5PLX5ua/J88URBJsiH8WEosBcTV/Yk7aQ1jT63/I8x+NrsJG1DILFy60+T4vFBtFNbJHADwVUmzLqECZWY3gq56GXsiAowXQeyUmQnvGz7KjVGo10tPSKJBOHIo6YTSaYR2FdknAxf1dE+JIKuURSPaaglENGzCh7hcUKeLQJPGBu2DtbL9IH/QI9sDOrCqklTUgt1GC8Fs/xra8ZgwcKNCIZgdGQXQnkFVSg6AO9vJKK6jkr8lH0/HQxkrk1pmgkIiwZKQvesurcfBgdbv3lZtrvQEh9uu1l1tGE8oQ5+VMfwMmiRLrEl7EgiM3IrZ2JwaUrsDh8Pld8r2JeyvWyvC9Yg68BoTwoa69JcXo1VcJUb9HL2p/2/7ajUM7NkNvss0wcIPeOvR0xLQ5GDU4qUP7snXZiOswnPqdSOoRjFFDB3Z4f3t3G7EjC9Abzn4QZRHLsDbhZczIeIwH0menP4SNcU8jPXg6uoJOZ/17ev7myZg+rGeH9rVuXyae+nzT6X1eLHaD76GUYO2RYqhiB+NTS3fM0JUhROkYD7tYD3QWQL/ukdcQEt2jw/srLziJb19dyvdLQXTiSDyTpvLXayJraLQjcRn7vSahh+44QoxFmFT7I1YF3Op2HZXUcimm9AlF73BvbDxahAYPX7y7rx77Kvfihcv7Ij7Ey95FJOdAQXQHVlVvzXV67wdsoqOOT3YkDYjEi8k6SH1MMDfVIW/ls7jjpayL3p/R6BgX0c6iM3rtndmTihBH56x/A1Ue8fir+/2YkPMaLsl7G2VefVDm1bfTvy9xXzurPXHvkWjUyqQwazXobUzH5IFsxNjFjxrz9O14mohz8fYPRGRMx0azdVbZiOvwUMoQFmDN1d0R3ir5eb9mDaS/ginZzyKxcgMuy3oGXoZy7I+4sctu7LuH+mFQzwibdJyxhdhATwxQVGN/jRSlHv64MtkTXwzORU9PPRwFC6BHxvexdzEI6RSVgjeUUX0hggVzIyiVC3EdFpEEf/hdi2sr3kCsPg29m/cj1WMY3FGknxqTwoz4/JtvETLxJp5K7bK3d2DxJbG4b2IcD7YTx0E/DQfWoLX2kpl0+Vz075PYoX3tzK5BSegYSJSeCJM24amEIwj97xQAbLkwy9al4MPVyTCbKYhur157+/btx85N6//Vk4oQR+bMfwNHQuciqj4F8dXbMDP9EXyX9DWa5QFd8r2J+7AIwIe5QXgjKxQWiBBoLMfhLx/DqHlXdyiATghpH4tYylO7aKV+GFT6Pcbkf4CAppPYFPckzBIl3JGXxIiybx5F0h1voljng6uS4/DRgHyMprQShHS6ZCGBv/aTFCPUQUaBEGIr1bIw7PWehjGatRhb/xvylQluldblTGyUiSb5Z/z42n+wMkeMzWnl+OjPk1hzpATPX94HE3qF2LuI5BQKojsB/8CgDvXyOqFRoiLGCxKRGOHmEvw+vhb+cj8AbLlw4fsyL7osxDa99jKyTtqsPIR0Naf8GxCJ8EfcM/BvzuP50dmQ/5/7fMADLoTYQpVegqXHo7Ctytrbdn5EDVSHf8IBTYW9i0aIexGJ8WfsEtSqojE+53UkVv2BwOaTPN1Lrbob3JGpvhy3izfgD++ZSKnzwKID3fF0QgkWRVe72+h7QrpMg0mMA0IcXx8vZ3MSBNu7SITYXIrneMRrj/C0LhPqVmKN/81ul9blTMEeUiy7YRA2pZbjv6tPoLhOi5u/TMG1w6Px5IxE6pXuAOgn4OI92nZVe+JgvQcgAprSd2BuxBH4yy+1d9EIIcTpGKUefLK5BUdvRKTmEC7NewvbYx+2d7GIC9hR5Yklx6JQaZBBLrbg2V4lWBBVgxcOU45wQuzlaNjVqFF1w/TMJxDUnI3rjlyPv7rdj6OhV/JAu7tRi/RYPiQHj6dG4pcSPzyTHoH0RiWeTSyBXCzYu3iEuJyVxX4wQAZDVQESu5VSEJ24JDa59x9+C3BdxZuI0x1HT+0hZKoHdfr3TUtLc4h9nM/k3iEYHReANzZm4rOdufguuQB7TlbjrfkDkBTl22nfl7SNguguymARYUO5D3KbFfx9cGMW9q/6H2R3TbJ30QghxGmxXohsqP+c9IcxsPRHVHj2QmrwTHsXizhxW/1GVgg+zrPeGMd76PBOUgESvTo2ISAhxDaKfIdg+YBvcVnmU4iuT8HEnFfRs2oztvZ4BO5IKRHwRt9CJHjq8EpmKL4vCkBOkwIfDsiHv5we+hFiy85wXxUE8vWGA2sg6t7xOSEIcVTVsnDs85qEkQ1/YEL9LyhUxEMr6ZxJNTU11nlDFi5caLN9NjZ2Tnoz1uv8qZm9MaFXMB5acQS5VU248sPdeGBiPO4c1wNSifs90HcEFER3QRqjGKvLfFFtkEEiEjA5qB4l5WwCUeolQgghHZUTMBZ7om7FyMJPMTH7JdQrwlHs0/k9JohryW2S4/6j0TiqUfP310VV48mEEqgk1FYT4kia5YH4uc/7GFC6AmPy30eU5gAWHl6AYOkQbFC535BzNsr+9u6ViPPQ8XNYcq0n5uyNw7KBeUjwcpwJRwlxZlsrvZDXrIASBjSd2AZcPcfeRSKkU7Egepz2GIJMJRhf/wvW+d/QKd9H26jhrzNufwIJ/Qd3aF9p+/7E+q/ehk7XuZ1fRscFYsMDl+CJ345j7dFSvLEpE9szK3mv9Ch/630E6ToURHcxxVoZ1pb5QmsRQy0xY1ZoHZ+EpMTeBSOEEBeyN2oxApuz+USjs9Mexo/9P0ONuru9i0WcgCAAv5T44qm0CDSbJfCRmvBq3yJMC7Fe1BNCHJBIjMPh1yDXfwzG5ryJHrU7cJk0Gbn3e2K7ZR1KDX3QJLf2GnUXE4Mb8MuIbCw+2A0FWgWuSI7DK32KMDus3t5FI8TpfZRrHaE2VJSJDCONTiOuzyKSYqPfNVhQ+RYStIeRqR2IbFX/Tvt+AeExiIzv06F9lBd03Rxdvmo53lswEJMSg/H0bydwIL8W09/ZgdfnJmFqn9AuKwehILpL3ZQf0aiwo8oLFogQJDdiVlgdvKQWexeNEEJcj0iM9fHPwcNwF8IbjuHy1AfwQ//PeI9FQlobKcaC56tKrRN7D/drxFv9CxGmNNq7aISQdqhXRmJ17zcRU7sHg46/hG6KMszCVphT/kJWwEScCJmFQp8hPMerPSmlQIhQiXDNEchNjVCYGyE3N0EsmM76nFGihlbqA53UB1qZLxoUoTCL5e3+Pj099Vg1Iht3H4nG7hov3Hc0Bvtrq/Bkr1IoKE86IRdlf62aT+ArF1kwSpSG5fYuECFdpEIehf2eEzC8cTOfZLRI3gM6iYe9i+UwRCIRrhgYiSEx/rj/h0M4WFCH2785gMVjuuORy3pBRuldugQF0V2A0QJsqfRGRqOKv+/pqcOkoHrI6G+IEEI6jVmixOrENzD/6C3w0xXi8tQlWNHvY3sXizgoNtH30uORKNHJeaq1B3qU467YCkjcLxsEIU4v328kfjEsRsNvj+N/1/RBgpCLXlV/8KVJ5o8cv0v4Z0q9+qBRHmLNgWLLnjPGJkCnAfSav19PrV8lrcF1T3gDwqvAsQvbtQVi1Csj+ISqbHRVqVc/ZAnNrW7jJzfj68G5+L+TIXg/JwTfFAbicL0a7ycVIFpt6FhdCXFDH57qhX5VRC28S7X2Lg4hXSrZewridMcQYCrHuPpfscHfdrnLXQVL4fLj7SPx6vp0LNuZy5eDBbV479pBCPe1xgRJ56EgupOrN0rwe5kPqgwyiCDgkoBGDPBptum1OiGEkHPTyvzwa++3cc3RmxHSlIaZGY/iQ+nN9i4WcSBaswivZobhy1MThMWo9HizfyEG+7YemCKEODoRVmeY4Cu+G1P6BaNP+Wr0qtoID2MN+lWs4gvTJAtAmVcfVKtj0SAPRiNbFMEwSNSwiGQwi2TwEhoQ5S2CNxoBTSlg1gOGJsDQCOgbra+GBuu6vgEQzj+Jp/LUPYAeMmiVwdBLPGGQeEIv9eTf7+/SW3jvdKWxHkpTPdTGWsgsOv5QmC0sZQ0zG8Add3hALk4Fan0B32g+GutMUjGwNL4cQ3yb8eCxKBzTqDFjTzyeTSzGFWF1dF9CSDud0CixtdIbYgi4vVsldpXau0SEdC3WJm70W4D5lW8jUXsAGdqB9i6SQ2K9zp+c2RtDuvlj6cojvFf6jHd24K1rBmJszyB7F8+lURDdieU1ybGhwgd6ixgqiRnTQ+oRqaIh4YQQ0pXqVVG8R/pVJ+5C99rduFZhxBsUMCAADtepsORYNHKaFfz9wqgqPNazDB6Uao0Ql1LhmciXP7svQaTmIGJrdiBccxhBTdnwMFajR81ffGnNew96AdgJHNrZju8oAhSegMLbuihPvSq8se5wKRa+uQGzHv0YAwaPa38lBAEehir4a/Pgr81FYFM2IjSHEKDNQ/8Qlp6mADhaAMjUQFACEJQI+ESd1ct+fFAD1o3Mwj1Ho3GwzoOf/zZVeOPF3sXwl58/8E8Isfq/7BD+OiO0Ht08DNhl7wIRYgdl8hgc9ByHIY3bMKnuJ3wvHmnvIjmsaX1D0TvMG3d9dwDHizW48Yt9uHd8HO6f1BMSMd2QdgYKojshiwDsrfHA/jqWH0qEUIUB00PrKf85IYTYSal3f6zp9RrmpC3BAP1+fDpLCQh0TnZbYilW6ZPwx744mAURQhRG/K9vIcYGNtq7ZISQTmQRy1DgO5wvjNSsQ3BTBkIaTsBHXwwvfQU8DRXw1FdAatFBIhghsZgghhl6kwBI5FAoVfwVcjUg9wLkLFjuaX1lCwuYs1fxufOu16IJtWwewgvt/i0SoUkRxJdC36Gn/ztn9+8o/fERvHJ1POLk1YCxGSg5ZF1U/kD4ICC0LyBV8s+Hq4xYMfQknxjxrZMhWF/ui/21HnzSUf+OHFxC3ODB++ZKH94L/YG4cnsXhxC72u09DT20x+FnrsQtISewwt4FcmDRAWqsvGMUnv89Fd8mF+Cdrdk4VFiHt68ZCH+P9s9zQtqHguhOOCkZ631eqrP+MfTzbsalgQ2Q0kMmQgixK5b/dl3Ci5iR/ij8lCKIWhluT1xXueCL0OtfxzpDHH8/J6wWzyWWwEdGvw+EuBuTRIkS7yS+tObI7m346sU7sPzxebhuxAA4Eo3ICz+nmXCFuR/iRvYD6vKBynTroq0BTm4Gcv8EQvoCUcMBlS9P73JPjwqMC2rAg0ejkNWkxOJD3TFCrYLEK8DeVSLEIb2RHcpfrwyvRQ8Pvb2LQ4hdmUVybPS7BvOq3sMU30JMjrXvhN2OTimT4MUr+mFoN3889ssx7Miq4uld3r9uEAZF+9m7eC6Fpp50IlmNCnxXFMAD6HKxBZeF1GFCEAXQCSHEUWQHTMCHvo9i7k9aCOK/c88S95jk+/2cILxvmQFFaBw8oMf7Sfl4u38hBdAJIa0S/pFj3GGx3u/+sUDCdGDE3UDcFEAdCFiMQOkhYN/HQPpaoLmGf7yvtxZrRmbh9m5sEmUBe5tDEX7Lh1iT0QiTmUZrEXLm5OM7qr0gFQm4vwf1QieEKVHE4rDHGL7+6SwVVKCHS225fGAEfrt7NGIDPVBar8P8j/fgq915ENik5MQmnOSKzb1ZIMaWSi+sK/fl+c9Z+pZrI6vR05NOIoQQ4mhy5Akw03WKW0nVKHFFchxeywqDGRI0Z+/DMx5reE5TQghxSVIFEDEIGHIL0H8B4NedPQ4Ayo8B+z8F0tYA2jooJQIeSyjD7yOzkKCohVihxhdHGnDZ2zuwKbWcbuyJ2zNZgOfSw/j6wqhqRKlpjjNCWuz0noEygxoxvmLcpNxs7+I4hYRQL6y6ZzSm9wuF0SzgmdUncN8Ph9GkN9m7aC6BgugOTh7SA8d9RuO4Rs0vTIf4NuHqiFr4yKj3BiGEEGJPBosIb2aHYPbeeN5O+0hNuFq0E5U/PwcfsdbexSOEkM7Hcq/7xQD95wMDFwH+PazB9IoT1mD6yS2AUYtELx1eDNuL6vVvw1MuQlZFI279OgVzP9qDlDxrz3VC3NGPxf7IaFTxa4gHqBc6IWcxiRV4p9SaEm2a/BDiqrfau0hOwUspw/vXDsJTM3tDKhZhzZESzHl/F7IrGuxdNKdHQXQHvjE/oB6B0EVvQiv1glpixuVhdRgd0AgJpW8hhBBC7OpovQqz9sThnZMhMAkiTA2ux6YxmRgozrF30QghxD68w4F+c4FBNwK+MQCbG6RoP5D8EVCYDAnMaDy6CR9OD8Zd43pAKRMjJb8WV3+0B4s+34fdJ6uoZzpxK/VGCd48lQv9wbhy+Mop/Rsh/3S0ORD/22XNwjA56wV46UrtXSSnIBKJcMuY7vjhthEI8VYgu6IRs9/bxQPq5OJREN0BndAoMWdvHA55DIdILIG/vhTXRVYjRm2wd9EIIYQQt6Yzi/BKZigu3xvHe44FyE089/lHA/IRrKBhkoQQAq9QoP81QL95gEcQYNYDOduQWPk7pvSQwEMuxn+m9cKfS8djwbBoSMQi/JVZiWs/TeY95dYeLaWc6cQtvJwRimqDFHEeOlwXVW3v4hDisJ7cqkeGORxKcwMuy3wKIoGuudtrSDd//H7vJRgZG4Bmgxn3fn8I/119AnoTPbS7GNKL2op04qRkwXgvx9qrTWHRomjN2xg+pj/U0sH2Lh4hhBDistIKKtv8zDGtPz6u7oMSoyd/f4lHCW4OSIOPxoBDGutncstq+WtWSQ2CMos7VKaSqlM7JYTYVINGg9LSjvVkq6urs1l5WrDzx8EOnjdazkG1dXUdrmPLfi6+bArAawr8pbmIaDgIpVmDPxZ6oG7fk0C39xHi3x0vX9kPd47tgWU7c7AipRBHi+px93cHEeqtxNwhkZg3JApR/iytJSGuZW+NB34oDuDrL/cpgoy6NxLSaqzs9eYr8J7vl4hoOIIRBZ9iT8yd9i6W0wjyUuCbW4bhzU2Z+GD7SXy5Ow/7cmvwzoKBiAu23teQ9qEguoM4WKfGE6kRSGtQ8feXhdTB/8QKvJS+ExjT32bfp7BS0+GLc7qpd62bQE0D5cUizsuWgRBbBRyqqqo6vA/SdVp+5gtfWnHez4hV3vCbsBiefYfz96aGatRs/ADLs5Ox/Dzb3PvBJgBs6TitgXrbEGILOqO111VKSgoOpXcs9ZKhIpe/Njc3d7hcjXXWHqhPfb6JL7awbetW/LXvcIf3Y6wptknZvBXAM2MVuG+4HL5lu4D3hwOj7wfGPIjoADWem9MX90+Mx9d78vHN3nyUaXR4d2s2X8bEBWL2gHBM7R0KH7Wsw3UixN60ZhEePxHJ16+NrMZQv46fRwhxdeWCHzb3eBwzMp/A8KIvUOQzBIW+Q+1dLKchlVhHgQ2O8cPDPx1BaqkGM9/dwfOmXzssmqd/IW2jILqd1RkkeDUrFD8U+UOACL4yE55LLMas0Hq8eNx2k5I1N1oDpf/7KZkvtkA39a51E2gy0c+TuPffgK0CDuZG6wRptgjIk87X8hBlxryFSIiPPetrLDVvnhCIY5ZoGPklk4BYUQX6+hZBNn8qALacbdtfu3Fox2aMmDYHowZbJ0K6WC37ouGWhNiG4dTfUlKPYIwaOrBD+9q724gdWYDe0PF0i/rmRv566ay5GNI/0SbnDVvUkdm6tQkH0mCTc9rWrBws+/A7bHl4MMK0GcBf/wOOfA9MfQlInIUATwUenNwTd4+Pw6bUcvywvwA7sqqwM9u6PCE5htFxgZjeLwwBejovEuf1YkYYcpoVCFYY8UhPul4kpL0yg6Ygun4f+pWvwvTMJ/Bd0tdoUFjnFSDtMzExBH88cCke+ukIb2Of+PU4tmdU4sUr+iLYS2nv4jk8CqLbiUUAVpb44dXMMJ4HjbkqvAaPJ5QioBMmFDHodfyVbupdgy1vAvnNURZgttDPk7j334CtAg7pGVlYc6RzhvqTzhMQHIzImJjT76sNEmyt9EaJTs7fB8qNmBikQSi/trT2HjsXT980/urtH3jW/i5Gy74IIbbloZQhLMC7Q/vwVlnPDbbkExBks/OGLerIqJVym53TquobsbbKgq0R9+G6QV7AH08A9YXAiuuB2PHA9NeAwHjIpWLM6B/Gl8KaZvx6qBjrjpUivayB3+izhQm94S0cr5MAtc0I81Hx3OqEOLrNFV5YXhjI19/oWwgfWdv5/22RIo6hEeXEFWzv/jBCGtMQ3JSJWWlL8WO/T2GWuEbwNy3Ndtf+er0eCoXivF+/f4AUPdReWH6sgT+43pNdgZsGeGNcjOpfvdIDAwMRHR1tk3IVFBTYdOS2LcvWHhREt4N9tWo8nx6OYxprfr94Dx1e6F2M4f5Nnf696abetdjiBqnl5ogQd/8bsFXAodzTNS7i3Hni0L01njiqUfERYlKRgJH+jRjg0wyKzxBCiA2wm/Pec4C4ycDON4Fdb/OJR/HBSGDkXcClSwGFF/8oy4d+38R4vmRXNPJg+sbUMhwv1kARGocMDZBxsBhyiRiRfipE+KkQ7qtCsKcCYjppEwdTpJXhP8ej+PrimEpcEmgdhXI+VfVNNk8Rx9CIcuLMTBIlVvd6HdceWYSQpnRMzX4O63q+AIicd2IBTY314fDChQttuFfWBgptfkoW1B0B0+8HQuPw7r56/O+HLaje8D7MDX/PF6VSq5GeltbhYDULoPdKTITWBqnwbF229qIgehcqbJbhlcwwrC335e89JWbc06MCN8dUQS5u+5ebEEIIIZ2DBcyP1Kt4AF1nsV6E9/DQ4dKABni3o5cYIYSQCyRXAxOeBJIWAH88DmRusAbUj64AprwA9L3KGnA/hU1+1hJQ37p7P2bf+h8MnP8gqoxyaI1m5FQ18YWRSUS8d3qEr3UJ8VbwfLCE2DMP+u2HuqHGKEVf72Ys7VnW5jYNWmu6qEmXz0X/Ph1L9cTQiHLiKhqUYVjb6xVceeJuJFRtQr0iHLu63QNnpW20jhKZcfsTSOg/uMP7S9v3J9Z/9Xa798cyZWRpTEitl0AVOwQxd3+OBG8zenpZUFV0Et++upT3Ho/uYKCa7YMF0K975DWERPdAR5UX2K5s7UVB9C5QrpPi3Zxg/FjkD6MghhgC5kfWYElcOYIU9BSYEEIIsSdlTBIOqIaiqco6O32A3IhLAxoRre54rmNCCCFtCOgBXPsjkLEB2PAIUJsH/HwLkPKFNcVLSO9/beKrlKApdTuGBd6HiLjuqGjQo6hWi+I6LUrqtNCbLCioaeYLIxGJeCCd9VRnQXUWYGdpYwjpCmyOlUdPROJEgwr+MhM+HpAPxQV0ovMP7HiqJ4ZGlBNXUuQzGJvjnsTUrGcxrPgrNCqCcSRsHpxZQHgMIuP72CS4fKH7YyHogU0GbEkrR0m9Dqn1UhTqpUgMiIethUT3sEk97YGC6J2I5VP9ODcYXxUEQH+qV9slAQ0873milzVHOSGEEELs5+ciFUKueRGs76JSbMEI/0b089ZS6hZCCOlqCdOA2HHA7neBHW8A+TuBj8YAw24Dxj0CqPzOuRnL3RrireTL4Bg/CIKAqkYDD6YXn1qaDWYeFGDLftTyQe5BXn8H1UEdc0kneiUzFKtK/SARCXh/QD4iVEZ7F4kQl5AaPBNeulKMKvwEE3Jeg0HigbTgGfYultPy95Dj6sGRyCxv5BN6N+hM2KeTIvT615FSosPAgcK/8qW7Gwqid4IqkxLPpoXh+6KA00PCB/s2YWl8GUZ0Qd5zQgghhLRPqNICwWJGhLkEs+IVUEoovRohhNiNTAmMXQokzbemeElbAyR/CBz5DhizBBh+OyBTtboLdoPPAuRsSYry5UH1Oq3RGlQ/1VtdozPx3utsOVTAJgKXI3jec11WTeI+Ps4Nwsd5wXz91T5FGEnxAEJsKjlqMVSmegws/RFTsp6DRSRFRtBUexfLabE2NCHUC7FBHjiYX4v9edVQhPfCSztr8dvJnbhnQhym9Ql123lHKIhuQyUNJvhPuxd3FY6FCdbgOct39lBcOcYFNpyZ0o8QQgghDmBEgB7Lnl+CcQvmQilxzmGFhBDicnyjgfnLgewtwMYngYpUYPMzQPLHwPjHAEviBQUE/NRyvvQJ9+H/16Aznu6lXlKrQ02zARZtQydWiLijD3OC8GpWGF9/tGcpro6otXeRCHE9IhG2d18CqVmHfhWrcFnmU5CZtTgeerm9S+bUZBIxhscGIMBQhu9+Xo2QMXORWqrBXd8e5BN5zx8ShauHRPL0aO6Egug2tOyQBl5JU8GynA/3a8TdsRW4JKCRgueEEEKIg5KIAGN1ob2LQQgh5FziJlpTvBz9Edj6IqApAlbfi0TPGMxJkFqTTV8EL6UMvULZ4s3fZ6efwCcffQ3gGhtXgLir3/QDsP5UAP2+HuW4vVulvYtEiOsSibE57nFYxFIklf2MySdfhNpYg32RN501QTW5cEoJUPfnl/jt5btxoNEbX+7K5XOQvLEpE/+3ORPjEoIxs38YxicEw89DDldHQXQbuqqXB3bv3IG3LjHj2v4e9i4OIYQQQgghhDg3sQQYcC3Q50pg/zJgx+tQNebjt2vUKKl9Gocq70BW4EQIIkmHggSm+nKbFpu4J6MgQeDs/2C9oR9//5/4UtwVSwF0QjqdSIytsY/AKFFjSPE3GF3wIXx1BdjS43GYxa4f3O1sXgoxlozsiTvH9sD646X4YX8h9uXWYGt6BV9Ydhc2L8nExBAM7eaPPuHeUMouvl12VBREt6E+wQpU/vwcek2+GwAF0QkhhBBCCCHEZvnSR90DDFyIsl8eh8fx5QhHIcIzn0BdwYdIibgeaUHTYZIo7V1S4qZONinwseUyeCT6QwwLXulTjHmRlMKFkC4jEmFHt/tQrwjD+Jw30KdiLQKbTmJtwsuoV0Xau3QuQSWX4MpBkXw5WdmIXw8WY3NaOdLLGrA/j+VQt57zZBIReod587lJ4oI90T3QA01NLG+Hc48MoCA6IYQQQgghhBDnoPJFSeJiTFr8Ib597gaM02+Fr64Ik06+jDF57yE1eCaOhF2NOlW0vUtK3IRFAL4r8sdLGWFohgTm5nosCUjGvMhQexeNELd0NGwu6pRRmJ75JEKa0nHdkYX4s/sSHMDFpQAj59YjyBMPT03gC5tjZGtaOf7MrMLhwlpUNRpwpKieL2eKfvgXrCuWwruuAGq5FGq5BHKJGHLpqaVlXSLmedmlEhFf//tVDIkdJzWlIDohhBBCCCGEEKdSqwM2e8xBdr8H0a/8VwwoXQFfXTEGlX7PlwKfoUgLno4s//EwSmmUMOkcJzRKPJ0WgQN11t+xWJTiry8eQcKjkwFQEJ0QeynwG4HlA77F9IwnENFwBFOyn0eYLBb7A8T2LppLivBV4fqR3fgiCALPm36osA7Hi+uRU9mE3KpG5Fc3wQQZtGZAq9EDYMuFk4hEPKguFmQImPkQuhIF0QkhhBBCCCGEOCWTRIVD4dfiUNg16Fa3B0mlK9G9dhei6/fzZYL4FZz0H4vMwMnI9x3OP09IR2U3KvBuTjBWlfrx9x4SMx6KL4Mscyu2NdbYu3iEEACNihD81O8jDCr5HqMKPkY/5OD4nR7YZvkNeYY+aJIH2ruILkkkEiHKX82X2Unhp/9/f8oBjJxwGa5/9hN4hkSjyWCG1mCGwWyBwWT5+9VkgZGtmy0wmYVTrxY+6ocxCwLMJvZGBInKq0vrRkF0QgghhBBCCCHOTSRGnt9ovnjripFYuR69KtbDX1eAXlUb+WISyVHoOwQ5fmOQ7zsS9coInkOXkPYwC8DOak98UxCAzZU+p/9/dmgtHu1ZhnCVEd9mUboIQhyJIJLiQMT1/GFq0v5HMUiShSnYCVPKHJwImYXDYfNQo461dzHdgkQsgrmhEv4KAZFBnhe8vdki8OC6dRFQnJuN5Z99Djw6C12FguiEEEIIIYQQQlyGRhmB5KjFSI68BSGNaehVuQE9av6Ej74E3Wt384VplAehyHsgjutD0SuQhviTfzNagMP1amwo98G6ch+U6uT8/0UQMClYg/t7lKOvt87exSSEtIHNk/Gm8RqUfvsoPlrUB/FCHpLKfuZLsfcAZAWMR7b/eDQow+xdVNJKEF4ilkApk/D3zQoBxqoCdCWHu1I4fvw4hg4dCj8/PyxdupTn0mnLypUrERMTg/DwcHz//fddUk5CCCGEWFHbTQghxCGJRCj36o0/Y5fg88G/4auBP2JHzD08cG4WSeFpqOQ91K9u+BrfX0VpXjqrzXcm9UYx9tWq8X5OEK5P6Y6krX0wd18cPssP4gF0H6kJN0ZXYfOYTHw6MJ8C6IQ4ma25Zrwguhs/9fkQ2f7jYIEYEZrDGJf7f1h8YDauPbwQo/I/RGzNX1AZKDUTceCe6Hq9HrNmzcLUqVPxww8/4L777sOXX36Jm266qdVG/LrrrsP777+P4cOH48orr8SgQYOQkJDQpWUnhBBC3BG13YQQQpyCSMSH7LMlJfIGSMw6hDUcR6TmIALLduKPk4fBpoIktm3zHYnJAjSYJKg3SlBpkKJEJ0epTobtlmEIntcXjzT2Qt1W9b+285WZMC6wAdNC6vmrUuJaDw4IcTsiEYp8h/DFU1+O+OotiKvejnDNEYQ0ZfClRb0iDFXqONSpolCnjES9MpKPYvJCE8SUDcztOFQQff369aivr8ebb74JtVqNl156CXfffXerjfKyZcswfvx4LF68mL+/55578M033+CFF17owpITQggh7onabkIIIc7ILFGeDqIU6Ufjzc1XUhC9E9r8zrCvWAffsTfis+pE/JzqBaNFBMPpRfz3uiCC1iyGxmgNnDearSkA/i0Mqu5A3anYeLjSgCSfZgz3a8Jw/yYkeOooWEaIC08+yienDr8WKmMtYmt2IFxzmD9k9dfmwUdfyhfUnr3dIiXw3pNeaLI8A/MBfxglShjFahglKhglahjFKj7iSRBJYBGJT72yRcpfW94LrCe8JB3RY+QYKGxBWFHOGd/l7BOPcNb7838tRJKK0JFyDBG2I6y48B81vrCTWWxzGYQRcgSfXAHorKnQLlZwcREeHCHHpc0b4Ft8uEP7ailbfqLUfYPoR44cwYgRI3iDzPTv3x+pqaltbnPZZZedfj9s2DA899xzrT49Z0sLdhHAaDSaDpe/sbGRv/6yMxUHsko6vL+U9CL+mpuXD3EHW+3yEmt5SgoLsS/loMPsy5HLRvW0//4cdV+OXDZH3Zc71bOgoJi/Njc3d7htadnekYdKO3vbzX5OTHZ2DgyGv7+Hq/1u0t+z/ffnqPty5LI5cj0LC63n+sKMY9inVHRsXyfTra/5edgnd5xrflvvr6V93LZt2+lz78XKz8/nr8dTdqOk6J8BggtXU1Z4+n7OHdrurmjzO7Pt3pdbA6+B07GmEgBbWsV+DqZTi5UMRnhCB280w0vUjMaqchw8eBhX9vHEsEglFCYj0AgYi4GdsC7tlZxqzc+7IeUkCmoNcJR4gCOfnx35vOWo+3Knetqyvc1LPcJfs48fgEGnPedn/gSb92AYX1SCDtEoRiiqEIRqBKGGv3qjEV5o2b4REn0j2CM65UWWqx+AaWMUgG4dkIkOGwBg5li2vzXA353qL9r4cQrg4AfQdPDH6Q3gv2xf1d8D1bAJoZ+sa9tuwYEsWbJEuOuuu876v8DAQKGmpua82wwaNEhYsWLF6ffHjx8X+vfvf97PP/PMM+yI0EILLbTQQovTLIWFhYKjorabFlpooYUWWpyr7e6KNp/ablpooYUWWuBibbdD9USXSqVQKM5+sqRUKnlPBTZxSXu2afn8+Tz22GNYsmTJ6fcWiwU1NTUICAiASCS6qKcVUVFRKCwshLc3e65CugIdd/ug424fdNzd99izJ+ENDQ188k1H5Yxtt6P+vG3N1erjinWi+jg2qo9jc9T6OEPb3RVtfkfbbkf9+XYmd6sz1de1UX1dm8bF6tvettuhguj+/v58srEzsUrI5fJWt6msrGz351mj/8+G39fXFx3Ffmlc4RfH2dBxtw867vZBx909j72Pjw8cmTO33e7yt+Zq9XHFOlF9HBvVx7E5Yn0cve3uijbfVm23I/58O5u71Znq69qovq7N24Xq2562WwwHMnToUOzZs+f0+9zcXJ5HjTXW7d3m0KFDiIiI6PSyEkIIIYTabkIIIcRdXEybTwghhLgKhwqiX3rppXxIwBdffMHfs9m+J02aBIlEgrq6OpjN5n9tc9VVV+GHH37AsWPHeDL5d955B1OnTrVD6QkhhBD3Q203IYQQ4h5aa/MJIYQQVyd1tBxry5Ytw4IFC7B06VKIxWJs376df43lWGM91QYMYPPM/i0pKQn3338/hgwZwvOxxcfH46677uqyMrMhas8888y/hqqRzkXH3T7ouNsHHXf7oWPvmm23u/y8Xa0+rlgnqo9jo/o4Nlerj7O3+bbmjj9fd6sz1de1UX1dm8LN6ttCxGYXhYMpKyvDgQMHMGLECD7xSHukpqaiuLgYY8eObTWvKiGEEEJsj9puQgghxD1cTJtPCCGEODuHDKITQgghhBBCCCGEEEIIIY7AoXKiE0IIIYQQQgghhBBCCCGOhILohBBCCCGEEEIIIYQQQsh5UBCdEEKcVEFBAVJSUmAwGOxdFEKIE8vOzuYTwJrNZnsXhRBCCCGEEEIcEgXR23D8+HEMHToUfn5+fAby9qSQX7lyJWJiYhAeHo7vv/++S8rpai7muDO7d+9GQkJCp5fPVV3McX/22Wfh7+/PZ2W+4oor0NDQ0CVldffjvmTJEgwaNAjXXnstunfvjvT09C4pq6u52HMNU1dXh7CwMOTl5XVqGYnjtOMsyHznnXfC29sbarUat956K0wmE5y1PhaLBXPnzsWll16Kyy+/HH379kVFRQVc4TorJyeH/4y6mi3rw7b19fWFSCQ6vbzwwgtw9p9Pc3MzYmNj8fPPP6Or2bI+48aNO+tn07J0NVvWSavV8nMCO8cFBQXh4Ycf5ucJZ60PuyZdvHgxQkND0a1bN7z77rudXHribud8dzuHutv5qKtjFs4QR7J1nR09ltBZcSlHvY/sjPpaLBaMGjUKb7zxBlwCm1iUnJtOpxO6desm3H777UJ2drYwffp04fPPP291m2PHjglyuVz49NNPhaNHjwpxcXFCenp6l5XZXY87k5KSIgQHBwsxMTFdUk5XczHHffny5UJ8fLyQnJwsZGVlCQkJCcLjjz/eZWV21+O+bds2ftzr6+v5+xtvvFG44YYbuqjEruNizzUtFi9ezK4qhNzc3E4tJ3GcdvyFF14Qhg8fLpw4cUI4dOiQEBQUJHzyySdOW58vvvhCGDNmjGAwGASz2SxMmDBBeOaZZ1ziOmvy5Mn877Mr2bo+GRkZ/Jqmtrb29MK+h7P/fJYuXcp/Pl3N1vVpaGg462fz0UcfCePHjxecuU7PPvussGDBAqGoqEjYt28f3/dnn33mtPW57rrrhHHjxvF2ml07+fv7C8uWLeui2hBXP+e72znU3c5HXR2zcIY4kq3r7OixhM6MSznifWRn1ff9998Xevfuze83XIFjtTQO5tdffxX8/PyEpqYm/v7w4cPC6NGjW93m/vvvF6ZOnXr6/VtvvSU88cQTnV5Wdz/ujY2NQnR0tPDcc89REL0Lj/vLL78s7N69+/T7p59+Wrjssss6vazuftzZMd++ffvp92+//bYwc+bMTi+rq7mYY9/izz//5BcLAQEBDnXxQzq3HX/ggQeEkydPnv7aokWLhDvvvFNw1vqsW7eOPwxo8eCDDwr33HOP4OzXWV9//bWQmJjY5QEVW9eH3Vxec801gr10xs+H7UOtVtslMNCZ1/XsIVSvXr2EXbt2Cc5cpxkzZghr16496yb/scceE5yxPiwYIJPJ+M39mcHHWbNmdWINiDud893tHOpu56Oujlk4QxzJ1nV29FhCZ8WlHPU+sjPqW1xcLPj4+AhbtmwRXAWlc2nFkSNHMGLEiNNDw/r374/U1NQ2t5kwYcLp98OGDcOBAwc6vazuftxlMhkfQnLJJZd0USldz8Uc90cffRQjR448/T4jIwPx8fGdXlZ3P+7smI8dO5avV1VV4fPPP+fD30jnH3tGr9fj9ttvxzvvvANPT88uKClxlHb8//7v//gQanud82xdn8suuwwDBgzg62w46U8//cTTujjzdVZ1dTUffrps2bJOLHnX1Gffvn18YSldgoOD8eSTT15QyilHqw8r+2233caH9O7Zs4d/1lWu61laBTb8ntXNmevUp08f/rfDhpmfOHECa9euxeTJkzu5Fp1Tn/r6ehiNRkRHR5/+mkQi4QuxD1c757vbOdTdzkddHbNwhjiSrevs6LGEzohLOfJ9ZGfU94EHHuApigoLC/nnXAEF0Vuh0Wh4ruEWLM8hu/Cqra1t9zYsh1dJSUmnl9Xdj7tcLkdEREQXldA1XcxxP1NmZiZ+/fVXfnFHuua4f/rpp/zmkOX6vPnmmzu5pK7nYo/9Sy+9hJ49e2L+/PldUEriqO34tm3beN7AhQsXdkLJu7Y+Tz/9NP+dnjFjBiZOnNhJpe+a+rD5ItjfZlcHMzujPqxdnTVrFp/09bvvvsNHH32EH3/8sZNr0Xn1YWVnDwXYAwF2o8we4rz22mudXIuuOR+wXNt33303upqt68QCGiw4x3KhsjkSrrzySowfP76Ta9E59QkMDERUVBRWrVrF/7+pqYk/KHSkIJy7cbVzvrudQ93tfNTVMQtniCN1ZpzGEWMJnVFfR76PtHV99+zZw9vdyMhInDx5EjfccAPuueceODsKordCKpXyCQ7OpFQq+WQe7d2mrc8T2xx3Yt/jziaLYEFcNnkT6zVAuua4L1q0CCtWrOC9M957771OLKVruphjn5aWxgNZH374YReUkDhqO86CMWxS0WeeeYZPduXs9fnPf/6DL7/8kt+gr1mzphNK3r6yna98rW1z5ue3bNmCHTt28BsUe7B1fdavX4+33nqL39BMmjQJ9913H590zFnr88knn/BJ4r799lu8/PLL/MaK9a7vqknEOuvvh7XB2dnZmDNnDrqarev01FNPYcyYMSgvL+cTlicnJ3fpZJy2rI9YLMZnn32Gxx9/HDNnzuS9G2tqarr0wSdx7XO+u51D3e181NUxC2eII3VWnMZRYwm2rq+j30faur6ffvophg8fjt9//x3PPfcctm7dig8++IA/BHRmFERvBZsluLKy8qz/Y40Ue9rS3m3a+jyxzXEn9j3uzz//PL8xcaTeEO5w3Fkjx24MWaPEbhRJ5x77lmG0L7zwAh+2T9y3HWe9KNgokIceegiuUB82nPTaa6/lQVqWHsoZ66PT6XDHHXfg448/hoeHR6eWu71lO7N87d2mtc+z3ofFxcVw1voUFRXxNqvFoEGDYDAY+BBfZ/75fP/997yHpD3ShNi6Tiw4x0amsN+1hIQEHqDryusLW9eH9TovKCg4HWR9+OGHee9OYh+uds53t3Oou52Pujpm4QxxpM6K0zhqLMGW9XWG+0hb/3yLioowffp03qOdYaPDWOcj1ivdmVEQvRVDhw7lQxBa5Obm8hxG7JervduwIbiUZqTzjzux33FnvRbffPNNng+0JX8W6dzj/vbbb/Oh/S1Yw0Y5Pjv/2LMb8Z07d/LcmyxHMVvY/7F8cWf+PIhrt+OsB8XGjRt54Iz1dHTm+jzyyCM8LY29ziW2rA/rocYuylkvvZa/T4a9sr9bZ6uPVqtFv379+GsL9jmWV9JZf9/YcN4z65Ofn89vrMLCwjqtDl1xXc9GhLEguj3Yuk6sN2BFRcXpr5WVlcFsNnda+bviZ8R60bE0Aex3jeVmJfbjaud8dzuHutv5qKtjFs4QR+qMOI0jxxJsWV9nuI+09c838h/nrMbGRv6wxNF+ry+YvWc2dWRGo1EICgoSPv/889MzQs+cOZOv19bWCiaT6V/bsBlsPTw8hKNHjwoNDQ3CgAEDhNdff73Ly+5ux73Ftm3bzjsLMrH9cU9NTeW/71999RX/fWdLy2zOpPOO++rVq/k2W7duFdLT04WkpCThlVde6fKyu9uxZ59nM6ifuURERAg7duzgv/vE9dtx9jenUCiETZs2nT7nNTc3O2193n33XSE+Pl7Yv3+/cPDgQSEqKkr44YcfnLI+Wq32X3+f7DKXvbKvOVt9mIkTJwo33ngj//m8+eabglQqFbZv394ldemM+nzyySdCjx49hOTkZCEzM1OYPHmyMH36dKetD3Py5ElBLpd32e9YZ9dpxowZwpAhQ4Tly5cLb7/9Nt/3448/7rT1Ycxms9CnTx/hs88+67J6EPc457vbOdTdzkddHbNwhjiSrevs6LEEW9bXGe4jbf3z3bhxoxAQECBs3rxZyMvLE66//nqhb9++gsViEZwZBdHbsGrVKkGtVvMfPvuFOnHiBP9/1kgfOnTonNuwkzu7oPb29hYGDx7cpTfY7nzcGQqid+1xf+CBB/jXzlzo+HfN7/sbb7whhIWFCYGBgcKjjz7KbxJJ151rWrDfd3YRRNyjHb/88sv/dc4bO3as09aHXcQuXbqU7ys8PJwHal3pOssefUVsWZ/8/Hxh/Pjx/MFNQkKCsHLlSqGr2fr37aWXXhKio6MFlUolTJkyRSgtLXXa+jDLli0Thg8fLtiTLetUWFgozJo1S/Dz8+OBjZtuuqnL72Ns/TP64osvhP79+9N1koNwtXO+u51D3e181NUxC2eII9myzs4QS+jMuJQj3kfaur7Lli3jHXaUSqUwYsQI3gHQ2YnYP/buDe/o2NChAwcOYMSIEQgICGjXNqmpqTxv5dixYx0ul5UrH3fScXTc7YOOu/3QsXd9rtaOU32oPl2J6uPY9XHFOrlafYh7/3ypvm1zt/o6+7Fwt3snqi85EwXRCSGEEEIIIYQQQgghhJDzoIlFCSGEEEIIIYQQQgghhJDzoCA6IYQQQgghhBBCCCGEEHIeFEQnhBBCCCGEEEIIIYQQQs6DguiEEEIIIYQQQgghhBBCyHlQEJ0QQgghhBBCCCGEEEIIOQ8KohNC2nTjjTfiv//9r13LMG7cOHz55Zd2LQMhhBBCbI9dY7BrDUIIIYQ4B2q7iTuS2rsAhBDH98EHH0AspmduhBBCCGm/vLw8dO/eHYIg2LsohBBCCGkHarsJOT8KohNC2qRWq+1dBEIIIYQQQgghhBBC7IK6lhLSyfbv34/hw4fDx8cHV155Jerr63HkyBF4eHggPz+ff+aGG27AggULTg+LuuyyyzB27Fi+zTXXXAONRvOvtCZvvvkmYmJisG7dutNf27BhA/r16wdfX18sXrwYer3+9Nf+7//+D+Hh4fDy8sL8+fOh0+n4/7MnzP/5z38QFBQEPz8/3HPPPf966nyudC5msxlPP/00wsLC0K1bN95b/cwyvv/++5g7dy6v56hRo1BeXt7msTKZTHj88ccRGhrKl2eeeeasrzc1NZ1zn6wsrNz+/v68Hk899VS7y7JkyRJ+vMaMGYObb74ZkZGR/P8bGhpwyy23ICAgAImJifjzzz/Pejo/fvx4vr+4uDisXbu2zboRQggh53L06FH07duXt2EPPvggevXqhXfffRerVq1CQkICb2smTpyIkpIS/vn09HSIRKKzljOHU7d2LXA+rB0LDg7mbV9L2/nYY4+1q/znu75QKpW8JxvTUs69e/fy90ajEffddx+/7hgyZMjp6yFCCCHEGThC283uVe++++7T7zdv3szbY4vF0ua21HYTcpEEQkinqa2tFQICAoRnnnlGyMvLEyZOnCjccsst/Gv33nuvsGjRIuHYsWOCj4+PUFxczP+ffZb9aX711VdCZmamMHz4cOG+++47vc+xY8cKI0aMEKZNmyasX79eqKqq4v+flZUlyOVy4dNPP+Xb9evXT3j++ef519LS0gSJRCL8+eef/GuDBg0SPvroI/41tg9fX1/h8OHDwtGjR4XIyEhhw4YNZ9Xjhhtu4OU60+uvvy7ExcUJBw4cELZt2yYEBQUJv/766+kyhoSECG+//TYvV+/evYWnnnqqzeP10ksvCT179hQOHTokpKSktHuf77//vhAVFSVkZGQIR44c4cc8OTm5ze3++OMPITo6mh+TO+64Q5gzZ87pn8NNN90kDBkyhG/z7rvv8mPU0NDAvzZ//nzhqquuEoqKivhxDA0NvYjfDkIIIUQQRo4cKbzwwgu8Hfby8hJ27twp1NTUCAqFQvjiiy+EkpIS4ZprrhHuvPNO/nmz2cyvL9jC2nfW5v3+++9tXgu0ZdasWcLTTz8trF27lrepTU1NbW7T2vVFXV0db5PZNU1LeU0mE/8aa5O7devG67x69WpeZnatQQghhDgDR2i72T17eHi4YLFY+Pu77rrrrLjB+VDbTcjFoyA6IZ1o+fLlPMDa0rCxgDULDLc0UGFhYcLgwYN5QLoFC1aPHj369PtffvlFiImJOf2eBYX79u0r6PX6s74Xa8SHDRt2+v2HH34oDB06lK/n5OTwhnLTpk2C0Wg8vTDbt2/nAeL9+/fzxt1gMJxuKFsLorNg97fffnvW9586derpMl5xxRWnv/bYY48JN954Y5vHKz4+Xvjuu+9Ov//rr7+E48ePt7nP+vp6vpSWlgorVqwQgoODha+//rrN7V599VVhwYIFpy9CWJ0YdhzYRcG6detOb8cC8SywwLDtL7/8cqGsrIy/12q1bdaNEEIIOReVSsUfAjPsITlrB1lbzNoY1r7s2LGDB7gnTJhw1nasrWZt3H/+8592XQu0hV0rsGuUxMREYeXKle3e5nzXF0xubi6/Ef+ncePGCa+99trp93PnzqUbcUIIIU7DEdpu1t6yYPyuXbt4vCEiIoKvt4XabkIuHqVzIaQTFRUVobKykg95YkOz5s2bx9+z4VIsVQt7z4aCsaFYZ4qKijq9HhER8a9UKHfccQfkcvm/vtehQ4f492HLww8/jIKCAv41NiTr448/5kOzWbqT66+/HrW1tfxrLG0MS+dy00038aHcbIhWe4aPFRYWIjY29vT7Hj16nP5+LUPBW7Cytmdikn/u85JLLkGfPn3a3Gdubi5PrzJ48GD88MMPfPgcS/HS1nYsFcvhw4f5z4MNU+vduzf/f/YzMhgMfGhby/Gsqqo6Xb8XXngBKpUK/fv35+X75Zdf2qwbIYQQci6sLdqzZw9qamqQlZXF2yLWTj366KP8GoC9siHUZ7ZrDEtdxv7vxRdfbNe1QFvYtQJLecbKccUVV7R7m/NdX7SmtLQU0dHRZ11DEEIIIc7CEdpuqVTK08Wye1GWQlYsFmPkyJFtbkdtNyEXj4LohHQill+bBXZZoJYtLBc6ayBlMhlvGL/++muMHj0azz///FnbsZzbZwaWWX7wM7Eg8bm+16xZs876Xps2beJfY7nYWN4y1riyfbOAcMv3zMnJ4Y3vsWPHcPz4cezatQsfffRRm3VjDSjbtgVbZznaW3h7e+NCsYcHZ9b9f//7H8+73tY+77//fp5Hvri4GD///DMCAwPP+vr5tmMNf0VFBc8F99lnn52+mGEXEwqFgueIPfN4XnXVVfzrLP/bhx9+yB9uvP7661i0aBHfDyGEEHIh2A03u/FmD7BZW8/ak6SkJHz33Xf84S5rb3bu3Mnb9zOx+VBYu8UeHLOb6PZcC7SFXSPs3r2bt8VsLpH2aO36gmE39C31PBN7aN+SJ5Zpb6CfEEIIsTdHartZpy8WRP/tt994Bz2Ww7wt1HYTcvEoiE5IJ5oxYwZvXPbt28d7Lq9cuRLTpk3jDRKbBIRNGvLNN99g2bJlvMFswRrfr776ij/VfvXVV08Hb1vDJiDdsWMH34YFgNnEJqx3OcOC41OmTOEB8pZJw9gkni0TkLAeZyy4r9Vqz/paa2699VY+2Sjbjk26+dZbb/Ee8h3BJvZk+2QXDgcOHOB1YJN6toXViT3pZ0/xWdCdXRC0p+f7yy+/zC9+2GiAlh4ELRcO1113Hf+5sHXWM33ChAlITk7mX2e9A1g52QUSO1Zs8pb2fD9CCCHkTBkZGbztZu3zyZMn+aThLe0aa1dYD7f169fzm9uWdoY9XGc37KyNZA/V6+rqTk9A3tq1QGtYW3bbbbfxto09JGZt6Zk3yufT2vUFwyYfV6vV+P3333mb2TI52ezZs3mgnj3AZ5Op0YguQgghzsJR2u6WEddNTU344IMPeEC9PajtJqQDOpAKhhDSDvv27eM5ztRqNc9txia8/Omnn3iObY1Gc1YeNJaLm+UeZ/nTxowZI3h6evIJSVo+x7AcamyyknNhOddZvnT2vcaPH88nCmnBJtNkOdhZ/jY2wWnLBJosdxubVJPlQWXf78orrzw9gWZrOdFZPje2T1YPNjknm9zzfGVk27YnXxrLxcZylrOc5qys7Li0Z58sn1tsbKzg5+cn3HrrrcLkyZP5xK1tbffzzz/zY8UWdjpkOdlbJiRlx5xNLurv78/LwvKnt2ATsLKfD9uOTeZyZt0JIYSQ9mLt/oABA3hbw/KTKpVK4fbbb+fzfLB5Rlg7wyYYZ20Xa6dZnlXWprE268yF5UFtz7XA+bAcp2wis5Y5URYvXswn0G6P811fnDk/DPs6m2zt0Ucf5f+n0+l4PdmcLGxCM3atQ3lVCSGEOANHabtbsMlLu3fvfkF1oLabkIsjYv90JAhPCLEt1guMDav68ssv7V0Ul8eGrLMedyNGjOC98Flu+PDwcLzxxhv2LhohhBA3wIZ1r1ixgo98Yr2+2Mio6dOn83RhF5MWjRBCCCHu0Xaz+1c2YprN18XSkZ6ZZ50Q0jkonQshpMu88sorpydM+eeyevXqLi/Pvffeyxc2+Uu/fv34kDr2nhBCCOkKbFJsNoS6b9++fPg0S6nC2kpb34Sz3Kzna3/ZA2Rbb0cIIYS4Kkdpu1namJ49e/KUoyxFaXu3I4RcPOqJTgjpMiz3G1vOhU1Uwp7kE0IIIcS2dDodysrKzvk1dtPv7+9v0+0IIYQQ0jHUdhPieCiITgghhBBCCCGEEEIIIYScB6VzIYQQQgghhBBCCCGEEELOg4LohBBCCCGEEEIIIYQQQsh5UBCdEEIIIYQQQgghhBBCCDkPCqITQgghhBBCCCGEEEIIIedBQXRCCCGEEEIIIYQQQggh5DwoiE4IIYQQQgghhBBCCCGEnAcF0QkhhBBCCCGEEEIIIYQQnNv/A8KTCXptCqlqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gaze_x_mean的均值比较:\n",
      "group\n",
      "ASD   -0.328128\n",
      "TD    -0.358650\n",
      "Name: gaze_x_mean, dtype: float64\n",
      "gaze_x_mean的中位数比较:\n",
      "group\n",
      "ASD   -0.332843\n",
      "TD    -0.359147\n",
      "Name: gaze_x_mean, dtype: float64\n",
      "\n",
      "gaze_y_mean的均值比较:\n",
      "group\n",
      "ASD    0.049392\n",
      "TD     0.057385\n",
      "Name: gaze_y_mean, dtype: float64\n",
      "gaze_y_mean的中位数比较:\n",
      "group\n",
      "ASD    0.056946\n",
      "TD     0.058820\n",
      "Name: gaze_y_mean, dtype: float64\n",
      "\n",
      "gaze_speed_mean的均值比较:\n",
      "group\n",
      "ASD    0.069800\n",
      "TD     0.060908\n",
      "Name: gaze_speed_mean, dtype: float64\n",
      "gaze_speed_mean的中位数比较:\n",
      "group\n",
      "ASD    0.068253\n",
      "TD     0.061470\n",
      "Name: gaze_speed_mean, dtype: float64\n",
      "\n",
      "expression_changes的均值比较:\n",
      "group\n",
      "ASD    0.253044\n",
      "TD     0.213766\n",
      "Name: expression_changes, dtype: float64\n",
      "expression_changes的中位数比较:\n",
      "group\n",
      "ASD    0.27525\n",
      "TD     0.23700\n",
      "Name: expression_changes, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 为ASD样本添加标签1，表示患有自闭症\n",
    "asd_df['label'] = 1\n",
    "asd_df['group'] = 'ASD'\n",
    "\n",
    "# 为TD样本添加标签0，表示正常发育\n",
    "td_df['label'] = 0\n",
    "td_df['group'] = 'TD'\n",
    "\n",
    "# 合并ASD和TD样本数据\n",
    "combined_df = pd.concat([asd_df, td_df], ignore_index=True)\n",
    "\n",
    "# 移除文件名列（不参与建模）\n",
    "features_df = combined_df.drop(['filename', 'group'], axis=1)\n",
    "\n",
    "# 查看合并后的数据集信息\n",
    "print(f\"合并后的数据集形状: {combined_df.shape}\")\n",
    "print(f\"ASD样本数量: {len(asd_df)}\")\n",
    "print(f\"TD样本数量: {len(td_df)}\")\n",
    "\n",
    "# 检查标签分布\n",
    "print(\"\\n标签分布:\")\n",
    "print(combined_df['label'].value_counts())\n",
    "print(combined_df['group'].value_counts())\n",
    "\n",
    "# 可视化两类样本在主要特征上的分布\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "# 视线X坐标均值分布\n",
    "plt.subplot(2, 3, 1)\n",
    "sns.histplot(data=combined_df, x='gaze_x_mean', hue='group', bins=20, kde=True)\n",
    "plt.title('视线X坐标均值分布')\n",
    "\n",
    "# 视线Y坐标均值分布\n",
    "plt.subplot(2, 3, 2)\n",
    "sns.histplot(data=combined_df, x='gaze_y_mean', hue='group', bins=20, kde=True)\n",
    "plt.title('视线Y坐标均值分布')\n",
    "\n",
    "# 视线速度均值分布\n",
    "plt.subplot(2, 3, 3)\n",
    "sns.histplot(data=combined_df, x='gaze_speed_mean', hue='group', bins=20, kde=True)\n",
    "plt.title('视线速度均值分布')\n",
    "\n",
    "# 表情变化频率分布\n",
    "plt.subplot(2, 3, 4)\n",
    "sns.histplot(data=combined_df, x='expression_changes', hue='group', bins=20, kde=True)\n",
    "plt.title('表情变化频率分布')\n",
    "\n",
    "# 视线X坐标标准差分布\n",
    "plt.subplot(2, 3, 5)\n",
    "sns.histplot(data=combined_df, x='gaze_x_std', hue='group', bins=20, kde=True)\n",
    "plt.title('视线X坐标标准差分布')\n",
    "\n",
    "# 视线Y坐标标准差分布\n",
    "plt.subplot(2, 3, 6)\n",
    "sns.histplot(data=combined_df, x='gaze_y_std', hue='group', bins=20, kde=True)\n",
    "plt.title('视线Y坐标标准差分布')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 检查两组之间主要特征的差异\n",
    "for col in ['gaze_x_mean', 'gaze_y_mean', 'gaze_speed_mean', 'expression_changes']:\n",
    "    print(f\"\\n{col}的均值比较:\")\n",
    "    print(combined_df.groupby('group')[col].mean())\n",
    "    print(f\"{col}的中位数比较:\")\n",
    "    print(combined_df.groupby('group')[col].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9507c9c",
   "metadata": {},
   "source": [
    "## 5. 划分训练集与测试集\n",
    "\n",
    "根据要求，我们将采用80%的数据作为训练集，20%的数据作为测试集，并且确保训练集和测试集中ASD和TD样本的比例均衡。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ee89324a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征数据中的缺失值数量: 0\n",
      "训练集形状: (215, 28)\n",
      "测试集形状: (54, 28)\n",
      "\n",
      "训练集中的标签分布:\n",
      "label\n",
      "0    116\n",
      "1     99\n",
      "Name: count, dtype: int64\n",
      "训练集中ASD比例: 0.46\n",
      "训练集中TD比例: 0.54\n",
      "\n",
      "测试集中的标签分布:\n",
      "label\n",
      "0    29\n",
      "1    25\n",
      "Name: count, dtype: int64\n",
      "测试集中ASD比例: 0.46\n",
      "测试集中TD比例: 0.54\n"
     ]
    }
   ],
   "source": [
    "# 提取特征和标签\n",
    "X = features_df.drop('label', axis=1)\n",
    "y = features_df['label']\n",
    "\n",
    "# 检查是否有缺失值\n",
    "print(f\"特征数据中的缺失值数量: {X.isna().sum().sum()}\")\n",
    "\n",
    "# 如果有缺失值，填充它们\n",
    "if X.isna().sum().sum() > 0:\n",
    "    X = X.fillna(X.mean())\n",
    "    print(\"已填充缺失值\")\n",
    "\n",
    "# 使用分层采样划分训练集和测试集，确保类别比例一致\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42, stratify=y\n",
    ")\n",
    "\n",
    "# 特征标准化\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# 检查划分后的样本分布\n",
    "print(f\"训练集形状: {X_train.shape}\")\n",
    "print(f\"测试集形状: {X_test.shape}\")\n",
    "\n",
    "print(\"\\n训练集中的标签分布:\")\n",
    "print(pd.Series(y_train).value_counts())\n",
    "print(f\"训练集中ASD比例: {sum(y_train == 1)/len(y_train):.2f}\")\n",
    "print(f\"训练集中TD比例: {sum(y_train == 0)/len(y_train):.2f}\")\n",
    "\n",
    "print(\"\\n测试集中的标签分布:\")\n",
    "print(pd.Series(y_test).value_counts())\n",
    "print(f\"测试集中ASD比例: {sum(y_test == 1)/len(y_test):.2f}\")\n",
    "print(f\"测试集中TD比例: {sum(y_test == 0)/len(y_test):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eddccebe",
   "metadata": {},
   "source": [
    "## 6. 训练多种分类算法\n",
    "\n",
    "接下来，我们将使用多种分类算法进行训练，包括：\n",
    "- 支持向量机 (SVM)\n",
    "- 随机森林 (Random Forest)\n",
    "- 逻辑回归 (Logistic Regression)\n",
    "- K近邻 (KNN)\n",
    "- 神经网络 (MLP)\n",
    "- 决策树 (Decision Tree)\n",
    "- 梯度提升树 (Gradient Boosting)\n",
    "- AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20b47657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练 SVM 分类器...\n",
      "SVM 准确率: 0.7778\n",
      "SVM 精确率: 0.7407\n",
      "SVM 召回率: 0.8000\n",
      "SVM F1分数: 0.7692\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.81      0.76      0.79        29\n",
      "         ASD       0.74      0.80      0.77        25\n",
      "\n",
      "    accuracy                           0.78        54\n",
      "   macro avg       0.78      0.78      0.78        54\n",
      "weighted avg       0.78      0.78      0.78        54\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练 Random Forest 分类器...\n",
      "Random Forest 准确率: 0.7593\n",
      "Random Forest 精确率: 0.7500\n",
      "Random Forest 召回率: 0.7200\n",
      "Random Forest F1分数: 0.7347\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.77      0.79      0.78        29\n",
      "         ASD       0.75      0.72      0.73        25\n",
      "\n",
      "    accuracy                           0.76        54\n",
      "   macro avg       0.76      0.76      0.76        54\n",
      "weighted avg       0.76      0.76      0.76        54\n",
      "\n",
      "Random Forest 准确率: 0.7593\n",
      "Random Forest 精确率: 0.7500\n",
      "Random Forest 召回率: 0.7200\n",
      "Random Forest F1分数: 0.7347\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.77      0.79      0.78        29\n",
      "         ASD       0.75      0.72      0.73        25\n",
      "\n",
      "    accuracy                           0.76        54\n",
      "   macro avg       0.76      0.76      0.76        54\n",
      "weighted avg       0.76      0.76      0.76        54\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练 Logistic Regression 分类器...\n",
      "Logistic Regression 准确率: 0.8148\n",
      "Logistic Regression 精确率: 0.8000\n",
      "Logistic Regression 召回率: 0.8000\n",
      "Logistic Regression F1分数: 0.8000\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.83      0.83      0.83        29\n",
      "         ASD       0.80      0.80      0.80        25\n",
      "\n",
      "    accuracy                           0.81        54\n",
      "   macro avg       0.81      0.81      0.81        54\n",
      "weighted avg       0.81      0.81      0.81        54\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练 KNN 分类器...\n",
      "KNN 准确率: 0.7222\n",
      "KNN 精确率: 0.6923\n",
      "KNN 召回率: 0.7200\n",
      "KNN F1分数: 0.7059\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.75      0.72      0.74        29\n",
      "         ASD       0.69      0.72      0.71        25\n",
      "\n",
      "    accuracy                           0.72        54\n",
      "   macro avg       0.72      0.72      0.72        54\n",
      "weighted avg       0.72      0.72      0.72        54\n",
      "\n",
      "KNN 准确率: 0.7222\n",
      "KNN 精确率: 0.6923\n",
      "KNN 召回率: 0.7200\n",
      "KNN F1分数: 0.7059\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.75      0.72      0.74        29\n",
      "         ASD       0.69      0.72      0.71        25\n",
      "\n",
      "    accuracy                           0.72        54\n",
      "   macro avg       0.72      0.72      0.72        54\n",
      "weighted avg       0.72      0.72      0.72        54\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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DAADu5TBHDwAAACGIRA8AALiXY3fmZfe3AwAAcDESPQAA4F6O3XP0KPQAAIBrOZYXerRuAQAALEWiBwAAXMsh0QMAAEAoItEDAADu5YjVSPQAAAAsRaIHAABcy2GOHgAAAEIRiR4AAHAtx/JEj0IPAAC4lmN5oUfrFgAAwFIkegAAwLUcEj0AAACEIhI9AADgXo5YjUQPAADAUiR6AADAtRzm6AEAACAUkegBAADXckj0AAAAEIpI9AAAgGs5lid6FHoAAMC1HMsLPVq3AAAAliLRAwAA7uWI1Uj0AAAALEWiBwAAXMthjh4AAABCEYkeAABwLYdEDwAAAKGIRA8AALiWY3miR6EHAADcyxGr0boFAACwFIkeAABwLcfy1i2JHgAAgKVI9AAAgGs5JHoAAAAIRRR6AADA1YmeE6DDX2lpaRIbGyspKSm+c1OmTJGqVatKZGSktGjRQrZs2eLXNSn0AAAAgkyLvLZt2+Yo8jZv3ixPP/20zJkzRzZs2CA1a9aU7t27+3VdCj0AAOBaThFJ9Dp37ixdu3bNcW7VqlUSHx8vjRo1kpiYGOnZs6ds2rTJr+tS6AEAAPdyAnj4YfLkyfLQQw/lOFevXj1ZvHixrF69Wvbv3y8TJ06UVq1a+XVdVt0CAAAEQHp6ujmyi4iIMEduOjcvNy30OnbsKA0bNvS957vvvvNrDCR6AADAtZwAtm4TExMlKioqx6Hn8uv777+XefPmSVJSkuzbt0+6dOkirVu3lqysrPx/vyx/3l3ElWzYL9hDABAgqcvHBnsIAAKkfJnwoH125b6zA3btLeNa5zvR89ICcevWrVK9enXp37+/eDweGT16tHlNS7bo6GjTzm3QoEG+xkDrFgAAuJYTwA2Tz1TUncmJEyfMalyvgwcPypEjRyQzMzPf16DQAwAAKIKaN28u3bp1M6tuK1SoYPbUq1ixosTFxeX7GhR6AADAtZwifAu02267TZKTk2XcuHGyc+dOqV+/vsyePVvCw/Pf6qbQAwAAKCKyL53QInTo0KHmOFsUegAAwL0csRqFHgAAcC2nCLduCwL76AEAAFiKRA8AALiWQ6IHAACAUEShhyKvbYvLZP284XLwh5ck6b3HpE5sBd9r0eeXkuRPhktMpbJBHSOAgjHv4w/ktjbXS8urr5AH7+0uO/7YFuwhwXJOAG+BJm4v9PS+bUuXLpVZs2bJzJkzzeMDBw4Ec0goYmKrlJPXhv9Lho6fIzVveEI2pe6SV5/s6ivyPnqpj1SvXC7YwwRQALb/8btMmzxJRox+Wd7+cJ5UrlJVRjz1RLCHBYS0oMzR+/vvv+WBBx6Q6dOnm/1iypUrZypfvc2H/rz//vvlxRdfLDLVMIKnbmxFGTp+rny4aJV5/vr7X8ns8X3N4+kje8jMz36UK+NigzxKAAXh1w0b5NLLLpc6deuZ561v6SBPPvZosIcFyzmW1xpBKfSeeOIJmT9/vsybN0+uu+46KVasmO+ebprq9ejRQy688EJ57LHHgjE8FCELvlqX43nt6hVk07Zd5vH9T78rqTv2yOjBtwdpdAAKUvUaNeWnH76T3zZukEqVK8vHH8yUJk2bBXtYQEgLSqH33nvvyauvviqtWrXKcd7j8ZjC75VXXpFHHnnktIVeenq6ObLLOpEpjuf/ikbYJzysmDx813UyfsZi81yLPAD2iK1RU1pc30p63tnRPK9UuYq8Nu2dYA8LtnPEakGZo7djxw5p1uzU/yvtyiuvlNTU1NNeIzExUaKionIcGf9dGYDRoqgY2reNHD56XKbO/jbYQwEQAOvXrZVvvlomk6a9IwuWrpCW/7xJBj/cN8ctoYCC5li+GCMoiV5mZqZcf/31vpZtbhkZGaaNezoJCQkyYMCAHOfKNx9SoONE0XFtk9pyX6fmcm230ZKRcfr/3wAQmr74fL5c/8+b5NL6ceZ57/sfko8/nCmbft0oterUDfbwgJAUlEJv6tSpppgLCzv7j4+IiDBHdrRt7VTtomh5M7G7PDJylmzY8mewhwMgQLKyTsjev/b6nh85fFjSjx2TEycygzou2M0pIsmbVYVez549Ze/evXLeeecF4+MRQkpEhMtH4/vIJ0vXytzFa6RUyeLmvLZwAdglrkFjGTH83zLz7elSNjpaPvn4QykbXU5q1qod7KEBISsohZ7Ot7C9gkbBaNmsrtSrWckc99x2te98ndZPyu87/wrq2AAULF2IkZqyRd5/9y3Zk7ZbYmvWkudGvSRhYeHBHhos5lhejjhZQZjlqqtrT1foeQtBncvnj5IN+xXA6AAURanLxwZ7CAACpHyZ4BXzFw9cELBrb3rxJnFloqeWLVsmZcqUCdbHAwAAiO0dxqAVenFxcczRAwAAsHHVbWRkZDA+GgAAwMfyQC84hV63bt2C8bEAAACuat0G5c4YAAAAsHiOHgAAQLA5dgd6JHoAAAC2ItEDAACu5fHYHemR6AEAAFiKRA8AALiWY3egR6IHAABgKxI9AADgWo7lkR6FHgAAcC3H7jqP1i0AAICtSPQAAIBrOZZHeiR6AAAAliLRAwAAruWQ6AEAACAUkegBAADXcuwO9Ej0AAAAbEWiBwAAXMuxPNKj0AMAAK7l2F3n0boFAACwFYkeAABwLcfySI9EDwAAwFIkegAAwLUcuwM9Ej0AAABbkegBAADXciyP9Ej0AAAALEWiBwAAXMuxO9Aj0QMAALAViR4AAHAtx/JIj0IPAAC4lmN3nUfrFgAAwFYkegAAwLUcyyM9Ej0AAABLkegBAADXcuwO9Ej0AAAAbEWiBwAAXMuxPNIj0QMAACgC0tLSJDY2VlJSUk56bciQIXLzzTf7fU0SPQAA4FqOU3SKvLZt2+ZZ5P38888yceJEWbNmjd/XJdEDAACubt06ATr80blzZ+natetJ50+cOCH33nuv9O/fX2rUqOH396PQAwAACLLJkyfLQw89dNL5SZMmydq1a6V69eoyd+5cOX78uF/XpdADAACu5QQw0UtPT5cDBw7kOPRcXnRuXm6HDh2SYcOGmSQvNTVVxo4dK//4xz/k6NGj+f5+FHoAAAABkJiYKFFRUTkOPZdfH330kRw+fFiWLFkiTz31lCxatEgOHjwob731Vr6vwWIMAADgWk4AF2MkJCTIgAEDcpyLiIjI9+//8ccfEh8fL+XKlTPPw8LCJC4uTjZt2pTva1DoAQAABIAWdf4UdrlVqVLlpDattnCvuuqqfF+D1i0AAHAtp4isus1LmzZtZP369WZBhqZ748ePN1us3Hrrrfm+BoUeAABAERQdHS3z58+XN998U2rXri0vvfSSzJo1S6pWrZrva9C6BQAAruUUkQ2TvbKysnI8v/rqq2XFihVnfT0KPQAA4FpOUav0ChitWwAAAEuR6AEAANdy7A70SPQAAABsRaIHAABcy2N5pEeiBwAAYCkSPQAA4FqO3YEeiR4AAICtSPQAAIBrOZZHehR6AADAtTx213m0bgEAAGxFogcAAFzLsbx1S6IHAABgKRI9AADgWo7dgR6JHgAAgK1I9AAAgGs5YnekR6IHAABgKRI9AADgWh67Az0KPQAA4F6O5asxaN0CAABYikQPAAC4lmN3oEeiBwAAYCsSPQAA4FoeyyM9Ej0AAABLkegBAADXcuwO9Ej0AAAAbEWiBwAAXMuxPNKj0AMAAK7l2F3n0boFAACwFYkeAABwLY/lkR6JHgAAgKVI9AAAgGs5YjcSPQAAAEuR6AEAANdymKMHAAAAqwu948ePy4MPPpjj3ObNm+WOO+7I870AAABFnccJ3BFShV54eLjMmTMnx7lffvnlpKIuIyNDSpcuXXAjBAAACGDr1gnQEVKFng543759Urt2bfn666/Nufnz50unTp1kx44dsn37dnMuLCzMFIUAAAAIgcUYR44ckXfeeUciIyPl448/lnr16sn+/ftl0aJFkpaWZh7379/ftHF79OghxYsXD/zIAQAAzpFTNIK34CZ6KSkp8tRTT8mxY8d8RdyYMWOkb9++8tlnn0mfPn0kOTlZ6tevL/fdd59kZmYGetwAAAAoiEJPEzxdeDF48GBp0aKFzJs3zxR4Dz30kJQsWdK8p3r16maxxtq1a037FgAAoKhzmKP3f/744w85cOCAWZBx5513yvDhw33p3vLly+Wqq66S8uXLm0IPAAAAIVTo6Ty9ZcuWSd26dWXYsGHSr18/U/idOHFCYmJiZMSIEWa+XqNGjQI7YgAAgALisXx7lXz3WA8ePCjr16+XypUry5YtW8zx6KOPyuHDh03bVou9uXPnyiWXXBLYEQMAAKDgEr2lS5dK27ZtpVq1arJ161YpW7asPPfcc/LBBx/Iv//9b/n777/NQoypU6eaghAAACAUOMzRE/nHP/5hWrO6V96kSZPMufPPP1969+4t27ZtM/vmLV682Mzfu+KKK1h1CwAAQoITwCNkCj1dRavbpnz77bcyYcIEmTlzpjnfrl07s7/eoUOHpGLFiuacztnT+XwAAAAILr/2QalTp47MmjXL/FTx8fHy+OOPS0REhO89Ho/H7LcHAABQ1HmKSIs1UPze8O7qq6/2PS5WrJgp9Lx0YUapUqXMeQAAAITI9iraktX98ryPq1at6ntNE7wnnnjCrLzduXNnYEYKAABQwBwncEdIJXpa3F1//fVmha22Z/X+tiopKUn+9a9/mfatzt+rUKFCIMcLAACAgi70dEFGiRIlcjxX0dHRZvXtwIEDadkCAICQ4hSV6K0ozNHTbVS80tPTzb1vvRISEsxPvfft3XffLTVr1izIcQIAACCQhV5WVlaOCjgqKuqk93z99deycuVK+eSTT/wdCwAAQKFy7A70/F9161W8eHFzV4wdO3bIqlWrpE2bNua8bpr84osvFuQYAQAAAsJjeaWX71W32qrV7VO8dFGG0vTurrvuMve4ff3116Vly5by1VdfBWa0AAAAlkpLS5PY2FhJSUnJ8/Ubb7xRpk2bFphCT+fnLVq0yNzL9rvvvpMaNWqYVm6ZMmVky5Yt8vTTT8tLL70kL7/8sl8DAAAAcPv2KmlpadK2bdtTFnlvv/22fP75535/v3wXerqlyrXXXmtatVOmTJFly5ZJ+/btpXPnzrJmzRq5/fbbzc9+/fr5PQgAAAA369y5s3Tt2jXP1/766y959NFHfXcmC8gcvSFDhkhkZKTs27dPfv75Zxk/frz88ssv0q1bN1P06aEyMjLMBsovvPCC34MBAABw4/YqkydPNm3bhx9++KTXtMjr0KGDHD16NHCJnm6IrAsw9NBYccyYMaZlO3v2bPNH0te978l+71sAAAA3Sk9PlwMHDuQ49FxetMjLy5IlS+TLL7886wAt34mezsFTv/76q0n1Jk2aJF988YVJ9rQK1eOGG26QYNr7w4Sgfj6AwKnR76NgDwFAgOyYdGvQPtsTwGsnJibKU089lePcsGHDZPjw4fn6fe2Q3nffffLqq6+aNRGFsr2K7p3XokULk+K1atXKHLpnng5CH+tcPgAAALdLSEiQAQMG5DjnT9fzmWeekSZNmvi2sDsbTlb2XZBD3LGMYI8AQKCQ6AH2Cmai99DHGwJ27fHt6/r9Oxqkbd26VapXr27aubt37/bddvbIkSPmcffu3WXixIkFn+jpsl7dP08/+NJLL80RLereLkuXLjXPdQNlve9tXFycf98OAAAAhu5LrItcvQYOHCjx8fGm0Msvvwq9Pn36SGZmpjzwwAM5Cj2tLn/66Sffc329adOmMnbsWH8uDwAAUKg8RWPRbZ6qVKmS43np0qWlXLly5gjYHL3ff//95IuEhUmJEiXM4w8//FBSU1PPalM/AAAANxd6WaeZUefvXTGUpyD3mtG07/HHHzf3uj3b1SEAAAAoGH4nejoR8M477zTz9CpXriwxMTFStWpV89rq1aulVKlS0qVLlwIaHgAAgP0bJge10NOdmN99913Zs2ePWf2hxZ2mdxs3bjR3xNi+fbvs3bvX3IdNN/YDAABAiBR6vXv3Npsj68qPatWqyUsvvSQLFiwwSV79+vXNe84//3xT8Om92j799FP20wMAAEWex+5AL39z9PS2G9u2bZPy5cub5zt27JC77rpLNm/e7HuP3vrsvffeM9uvcJ9bAACAECn0LrroIgkPD/f1sTXh69Gjh7Rr10769+8vM2fONOf19dGjR8uoUaPMXD4AAICizHECdxQFZ9Vf1dt56L1vZ8yYIdOnT5eGDRv6Xrv88sulRo0aMmfOnIIcJwAAAAJZ6OneLiNGjDBpnd6rbcyYMeYet7Vr1zaLM7w6dOggH33E7YoAAEDR5nGcgB0ht71Kq1atZM2aNWYLFV1skZSUZObmpaenm5W5XldeeaVUqFAhEOMFAAAoMB6xm1+F3uuvv57juRZ5SufvffbZZ5KcnCyXXHKJtGzZsmBHCQAAgMAVsidOnJBPPvkk74t4PNKsWTO54oorzPPvvvtODh8+7P9oAAAACpHDYoz/n5/37LPPnvJ1TfX0UA888IBMnTq1YEYIAACAwLZuixUrZgq5uXPnSr9+/aREiRInFYKa7Gmat3PnTunVq9fZjQgAAKCQeIpK9FZU5iAeO3ZM7rjjDtPK1X30brrpJlPk6VYr+lP31Bs4cOBJhSAAAACK8GIMr+rVq0tkZKTExsaa1bYlS5aUpk2bmtfq1asn7du3L+hxAgAAFDjH7kAvf4WeJnUjR46UQ4cOmfvZelfb5oWWLQAAQAi1bvX+tatXr5YNGzaYDZO9xR8AAEAo8ziBO0Km0NMET+fe6fYpEyZMMOe8973Nbdy4cbJx48aCHSUAAEAAeCy/M4bfizG0wNuzZ48cP35c0tLSfI+3bNliXtf2bmJiYiDGCgAAgEAvxtBCTlfVjh071neucePGpggcMGCAufetFoDR0dFnc3kAAIBC4RSN4K1obJisc/U6depkVtru3bv3pEO3XNHVuHfddZfZbgUAAAAhkOhlZmZK8+bNT/l6RkaGOVT37t3NfnsAAABFmcfyRC/fhV5YWJiMGjXqtHfOmD17tnlcp06dghkdAAAACneOXl50fl6rVq0K6nIAAAAB54jdkZ7fq24BAADgskQPAAAg1HjsDvQo9AAAgHt5LC/0aN0CAABYikQPAAC4lmP5jskkegAAAJYi0QMAAK7lsTvQI9EDAACwFYkeAABwLYdEDwAAAKGIRA8AALiWx/JIj0IPAAC4lsfuOo/WLQAAgK1I9AAAgGs5JHoAAAAIRSR6AADAtTxid6RHogcAAGApEj0AAOBajt2BHokeAACArUj0AACAa3ksT/Qo9AAAgGt5LO/d0roFAACwFIkeAABwLcfuQI9EDwAAwFYkegAAwLU8lkd6JHoAAACWItEDAACu5dgd6JHoAQAA2IpEDwAAuJZH7EahBwAAXMuxvHdreyELAADgWhR6AADAtZwAHv5KS0uT2NhYSUlJ8Z2bM2eO1KhRQ8LCwqRBgwaSnJzs1zUp9AAAAIJMi7y2bdvmKPI2b94sPXr0kJEjR8r27duldu3a0qtXL7+uS6EHAABcvWGyJ0CHPzp37ixdu3bNcU7TOy3yOnXqJBUqVJC+ffvKqlWr/LouizEAAACCbPLkyaZt+/DDD/vOacKX3caNG6VWrVp+XZdCDwAAuJYTwGunp6ebI7uIiAhz5KZF3ukcP35cRo8eLQMGDPBrDLRuAQAAAiAxMVGioqJyHHrubAwbNkxKlSrl9xw9Ej0AAOBaTgAjvYSEhJMSuLzSvDNZvHixvPLKK5KUlCTh4eF+/S6FHgAAcC0ngJXeqdq0/ti6dat06dLFFHr16tXz+/cp9AAAAIqgo0ePmgUZ7dq1kw4dOsihQ4fMeW3h5rdAZY4eAABwLU8Aj3O1cOFCWb9+vVmRW6ZMGd+Rmpqa72uQ6AEAABQRWVlZvsea5GV/fjYo9AAAgGs5gVyNUQTQugUAALAUiR4AAHAtR+xGogcAAGApEj0AAOBaDnP0AAAAEIpI9AAAgGt5xG4UegAAwLUcWrcAAAAIRSR6AADAtRyxG4keAACApUj0AACAazmWR3okegAAAJYi0QMAAK7lsXyWHokeAACApUj0AACAazl2B3oUegAAwL0cWrcAAAAIRSR6AADAtRy7Az0SPQAAAFuR6AEAANfyMEcPAAAAoYhEDwAAuJZjd6BHogcAAGArEj0AAOBajuWJHoUeAABwLYfFGAAAAAhFJHoAAMC1PHYHeiR6AAAAtiLRAwAAruUwRw8AAAChiEQPAAC4lmN3oEeiBwAAYCsSPQAA4FqO5XP0KPQAAIBreeyu84Jb6KWkpMiuXbskKytLKlSoINWrVw/mcAAAAKwSlDl6I0aMkIoVK0rNmjUlPj5emjVrJjVq1JDKlSvLyy+/HIwhAQAAl7ZunQD9nysTvRdeeMEco0aNkhtuuEEqVaokjuPIf//7X/n8889l0KBBUrJkSenVq1dhDw0AAMAqhZ7oTZkyxaR2vXv3lpiYGAkPD5ewsDCT5vXs2dO8Nnbs2MIeFkLEnNkfyeWX1jnp0PMAQlPZUsUl6dkbpEp0pO9c16ury48jbpTN42+RDwY0l5hy//8aUNDbqzgBOlxZ6KWmpkqrVq1O+XqLFi1k06ZNhTomhI7WbdrKVyt+8B0Lv1wmF1xwgTRqfEWwhwbgLIu8Nx+4SmLKlfKdq1aulPRvU1d6vJok1wxbJKm7D8u4bvwbB0Kidfv333+bNE+TvLwcP35cMjIyCntYCBHhxYubw2vWe+/Idde3kqoxMUEdF4Cz82qvK+XjH7ZJ4xplfefqx0TJyq1/ydpt+8zz975Nkdd6Nw3iKGEzR+xW6IXesGHDzvieJk2aFMpYENrS09Pl7RnTZca7s4I9FABnaeCMn2TbniPyzB2X+879uvOg/KPOhXJplSj5Pe2wdLu2hixP3hXUcQKhqsgUen/88Yfs27dP6tata+bs5ee/5PXILqtYhERERBTYWFG0zf90nlx2WZxUrlwl2EMBcJa0yMvtt50H5ZOftsuiJ643z7V12+b5JUEYHdzAU1Qm09kyR2/nzp3SsWNH6dChg+/ckCFDzB56jRs3llq1asnatWvPeJ3ExESJiorKcYx6PjHAo0dR8v7M9+T2O7oEexgACliD6hdIq7hK0mbkEqnzyFz5+MdtMqPf1cEeFizlBPBwZaHXrVs3OXHihC/ZmzFjhkyYMEG+/PJLOXbsmNx1113Sr1+/M14nISFB9u/fn+MYNCShEL4BioLfU1Nl2++/S3yzq4I9FAAFrH2TKjLnhz9kVcpeOXgsQ56fs16qXVjKtHIBFPHWbVJSkiQnJ5vtVDIzM+XJJ580id61115rXu/Tp4+MGzfujNfRFm3uNu0x1nC4xsLPF8g1LVqcclEPgNBupZUt8/+LrkqXCJOSxYuJx/Z7VSE4HLFaoSd6tWvXNumdGjlypOzdu1f69+/ve3358uVmE2XgdL75+iu5osmVwR4GgAD4blOatG54kfS+/mLp0KSK/KdPvOw+cEyS/9gf7KEBIafQEz1t07Zv317uu+8+8Xg8Mn36dClTpoxs2bLF3A1jxYoVMnXq1MIeFkKItvjX/rxGnhz+dLCHAiAAPv1ph9Sq+Kv0vu5iKR9VQjbuOCD3TEqSjBNZwR4aLORYHuk5WVlZhf4v5+jRo6Z9W7VqVbnwwgt9izTeeustKVGihHmsiy38ResWsFeNftz9BLDVjkm3Bu2zv9scuKS4ac0o9yV6Su9l26hRI/P4l19+kUWLFsnChQtl2bJlZkPlpk3ZGBMAAASeY3egV/iF3u7du01h5y3u9uzZIw0bNpRVq1bJ5MmTzbYrpUuXLuxhAQAAWKfQC72KFSv67mmrhV3Lli2lePHi5n6luvKWIg8AABQWR+xW6IWepnjeRK9du3ZmFW58fLy5y8WuXbskhnuWAgCAwuKI1Qp9e5Xrr7/ebKuycuVKs+hi6NChoutBoqOjzdy8OnXqyP3331/YwwIAALBOUBZjeJUrV046d+5sDqUrcT///HP54osvgjksAADgEo7lkV5QC73cLrnkEnM88sgjwR4KAABAyCv01i0AAEBR2l7FCdDhr7S0NImNjZWUlBTfuXXr1kmTJk3MotVBgwaZ6W7+oNADAAAIMi3y2rZtm6PI04WqN998szRu3Fh+/PFHWb9+vUybNs2v61LoAQAA13ICePhD1yt07do1x7kFCxbI/v37ZcyYMVKzZk0ZMWKEvPHGG6E7Rw8AAMAW6enp5sguIiLCHLnp3sLatn344Yd959asWWO2oIuMjDTP4+LiTKrnDxI9AADgXk7gjsTERImKispx6Lm8aJGX24EDB3KcdxxHihUrJnv37s331yPRAwAAruUEcHuVhIQEGTBgQI5zeaV5pxIWFnbS+0uUKCFHjhwxizPydY18fxoAAADy7VRt2vwqW7asWXWb3cGDB82tY/OL1i0AAHAtpwhtr5KbbquyYsUK3/OtW7eaOX9aAOYXhR4AAEARdM0115h5elOnTjXPddVty5YtzTy9/KJ1CwAAXMuRokvn6E2ZMkW6dOliNkv2eDyydOlS/64RsNEBAADAL7nvfHHLLbfI5s2bZeXKlWarlejoaL+uR6EHAADcy5Eir2LFitKmTZuz+l3m6AEAAFiKRA8AALiWEwqR3jmg0AMAAK7l2F3n0boFAACwFYkeAABwLUfsRqIHAABgKRI9AADgXo5YjUQPAADAUiR6AADAtRzLIz0SPQAAAEuR6AEAANdy7A70SPQAAABsRaIHAABcyxG7UegBAAD3csRqtG4BAAAsRaIHAABcy7E80iPRAwAAsBSJHgAAcC3H7kCPRA8AAMBWJHoAAMC1HLEbiR4AAIClSPQAAIB7OWI1Cj0AAOBajuWVHq1bAAAAS5HoAQAA13LsDvRI9AAAAGxFogcAAFzLEbuR6AEAAFiKRA8AALiXI1Yj0QMAALAUiR4AAHAtx/JIj0IPAAC4lmN3nUfrFgAAwFYkegAAwLUcsRuJHgAAgKVI9AAAgHs5YjUSPQAAAEuR6AEAANdyLI/0SPQAAAAsRaIHAABcy7E70KPQAwAA7uWI3WjdAgAAWIpEDwAAuJZjeaRHogcAAGApEj0AAOBijtiMRA8AAMBSJHoAAMC1HLsDPRI9AAAAW5HoAQAA13KCPYAAo9ADAACu5Vhe6dG6BQAAsBSJHgAAcC3H8uYtiR4AAIClKPQAAIB7OQE8/DBlyhSpWrWqREZGSosWLWTLli0F8vUo9AAAAIJo8+bN8vTTT8ucOXNkw4YNUrNmTenevXuBXJtCDwAAuJZTBAK9VatWSXx8vDRq1EhiYmKkZ8+esmnTpgL5fhR6AAAAQVSvXj1ZvHixrF69Wvbv3y8TJ06UVq1aFci1WXULAABcywngotv09HRzZBcREWGO3IVex44dpWHDhuZ5bGysfPfddwUyBhI9AADg6u1VnAD9X2JiokRFReU49Fxu33//vcybN0+SkpJk37590qVLF2ndurVkZWWd+/fLKoirFBHHMoI9AgCBUqPfR8EeAoAA2THp1qB99u6DgSseziuema9Er3///uLxeGT06NHmuZZm0dHRpp3boEGDcxoDrVsAAOBeTuAunVdRl5cTJ05IWlqa7/nBgwflyJEjkpmZec5joNADAAAIoubNm0u3bt3MqtsKFSqYPfUqVqwocXFx53xtCj0AAOBaTrAHICK33XabJCcny7hx42Tnzp1Sv359mT17toSHh5/ztZmjByAkMEcPsFcw5+ilHQpc8VCudPDztOCPAAAAwMLtVYoCtlcBAACwFIkeAABwLadIzNILHAo9AADgWo7ddR6tWwAAAFtR6AEAAFiKQg8AAMBSzNEDAACu5TBHDwAAAKGIRA8AALiWY/n2KiR6AAAAliLRAwAAruXYHehR6AEAAPdyxG60bgEAACxFogcAANzLEauR6AEAAFiKRA8AALiWY3mkR6IHAABgKRI9AADgWo7dgR6JHgAAgK1I9AAAgGs5YjcKPQAA4F6OWI3WLQAAgKVI9AAAgGs5lkd6JHoAAACWItEDAACu5dgd6JHoAQAA2MrJysrKCvYgAH+lp6dLYmKiJCQkSERERLCHA6AA8e8bKDgUeghJBw4ckKioKNm/f7+cd955wR4OgALEv2+g4NC6BQAAsBSFHgAAgKUo9AAAACxFoYeQpBO0hw0bxkRtwEL8+wYKDosxAAAALEWiBwAAYCkKPQAAAEtR6AEAAFiKQg9F3rRp08RxnJMO7/lixYpJTEyMDB48WI4fPx7s4QLIhy+//FI8Ho/897//9Z1LSkqSxo0bS5kyZaRly5ayfft2c3748OHm33p4eLjUqlVLnn/+eWF6OZA/FHoo8rp27Sp79+6V5cuXm+f6WI+MjAypX7++7NixQ15++WV5++235f777w/2cAHkw8KFC02xtmjRIvP8yJEj0q5dO+nXr5+sX7/eFHsPPvig7/2tW7eWlJQUGTp0qIwYMUKee+65II4eCB0UeijyihcvLueff775D36lj/UICwszaV6FChXMf0FMnTpVpk+fLn/99VewhwzgDLTAa9Giha/QS05Oln379kmPHj2katWq8uSTT0pmZqbv/ZrmVa5cWe6++24ZNWqUjB8/Xk6cOBHEbwCEBgo9WOO6664zP1evXh3soQA4jd27d8uaNWvk0Ucf9RV6WtxpK/fZZ581aX3Dhg1lzpw5ef7+DTfcYK6hCR+A06PQgzU04StXrpzs2rUr2EMBcBpffPGF1K5d28zD27Nnj6xdu1bKly8vb731lrz44oty8cUXm8enUqlSJfOTf+vAmVHowSo6YZtJ2kDRn58XHx8vJUqUMMmdN9Xr2LGjpKamSvfu3eXee++VQYMGnfLfueLfOnBmYfl4DxASdD5PWlqambMHoOjSwk7/rc6ePdsswtA5t507d5ajR49KzZo1zSpbnb+n0zGyL8jw8q7U5d86cGYkerDGsmXLzM9GjRoFeygATkFX1Oq2KfrvVefTTpkyxayo11Ztr169fO+75pprzHQMXaCRVyJYsWJFiY2NLeTRA6GHRA8hn+Lp/7r/4YcfzNYqffv2NekAgKJJizSdg9e0aVPzPDo6Wu655x7Txv3222/l3XffNUXea6+9Zubi1a1b17zv77//9hWIAwcONKtyvS1cAKdGoYeQtm7dOrnoooukWrVqct9998ljjz0W7CEBOEPb1rtCXum2SVdeeaXZD1O3SBo2bJh57F11q9srqfnz50v16tVNkZiYmGj+vQM4MyeL2awAAABWYo4eAACApSj0AAAALEWhBwAAYCkKPQAAAEtR6AEAAFiKQg8AAMBSFHoACpze1iojIyPHuRMnTpjz6vDhw/m6zh9//CG//PJLgYxJN9xdsmSJGUd2ev38jgcAQg376AE4J3orqqioKClZsqTs379fbr/9dnNrK72LwaFDh2Tv3r1So0YNcxcTvQvC4sWLJS4uTtq1ayfPPPOMjBw5Mkfxddlll5nX1EsvvWTuejJjxgzf60OHDpVjx47JqFGjzDW1gNO7KmSn5zwejxQrVsx3Tu+0kJCQIJ988onZpDcyMlKqVKli7q2qm/T27t3bjEN/NyIiolD+dgAQaNwZA8A5+fPPP83PzZs3S7NmzaRHjx5yySWXmHOTJ0+Wr776SqZPn57jd/Rm9h07dpQ2bdqYQm/MmDHmvN4C6/fff5fKlSubn1pw6f1Os9OCUos4pddu1aqVKdq8t8PSJPHo0aPy5ZdfSosWLcy5jRs3miJP76Jy4403mvFde+21pkA9cOCA/Pvf/5YBAwaY51pkvvLKK4XwlwOAwKPQA3BOtO35+OOPm8JsxIgRviJP6X2I9ZZVudWqVUt++uknk7jpLa46dOhgCjO9x2lSUpL8/PPP8s0330iTJk1yFJSaGOpN7tPT02XDhg3mNlmawJ3O119/LZ06dTKFnN4PWW+jpbfh+u233+Tmm282iaEWh1rg/frrr74iEgBswH+iATgnWqhpEbZ8+XL517/+5UvatKDSdqnev7R+/fqmffv+++/Lli1bTLqWnRZw2jr10mIr9w3rteU7evRomTZtmkn+tHWbnJx8ynFpW1fVqVPH/N4TTzwhZcuWNZ+vSaOmfa+++qp5/dJLL5X//Oc/J30mAIQ6Cj0AZ02LKZ3X9vbbb0vfvn3FO+VXCyYt9LZt2yazZs2SlStXylVXXWXeq61XXRShCZo3jQsPDzdt09Pp2rWrTJo0yaR52vJ98cUX5d5775VSpUrJ+eefb35qC1cf6xy8mJgY83sXXnihdOnSxTzW+YJPPvmkTJgwQd577z159tlnzZw/TQk1TaTQA2AbCj0AZ23VqlVm8YS2azU101artmO9q2u18GvatKlvVau+VqlSJdM6ve6660yB5y0YtQA8E23v6pw6nfPXrVs38/l6bS3UNCXU1qw+PnjwoFkMorQtq23gQYMGSWxsrCkU9fe0WNS27nnnnSeNGjUyReOcOXNk06ZNAf2bAUBhotADcNauuOIKM69t3rx5pgX60UcfSb169UzxpLTg08faMs1OEzdN9LQQ1CJv165dJnnL3nLNy/jx480q2dtuu83M5Vu2bJnMnTvXV9QpHc+KFSt8z59//nmzYEPTvP79+8tbb71lCr5y5cqZRR+64rZBgwZmcYa2mXO3lQEglLEYA0CBqVChgrz77rsmUVO7d+82K129sm+jcscdd0i/fv3MCtn169f73qdbp+j7chd8ui2KJnE6l08LRG296mIMnfuXfVWvFps6B2/t2rWmlTtw4EB58MEHTcF5/PhxU9jpPD1dPOJdlatz9bTQu/POOwP+NwKAwkSiB6DA6Dw7XXih7VH1/fff51iFq0Wc0laqFlq6515qaqp8+umnpsUbHx8vU6ZMMe1WLcqye/31100B593jTlu/X3zxhZQvX17atm3re9+jjz5q3vPcc8+Z5/pYi7ydO3fK5Zdfbq6tn+lN7lJSUkzSx5aiAGxEoQegwGlBNXbsWLMfXevWrWXmzJlm8YRuo6I0VRs8eLBZQKFz7jSt0zRQU7UdO3aY+XK6wCM7Tepuuukm33NdyKHXeeSRR3IsotB5f7rCduLEib5kUencQN3qRVu9V199taxbt86kiboSWAvOqlWrFsrfBgAKE4UegHOihZoWZ9k3NtY5eDrfTVO5W2+91bRWteWq7Vg9/+OPP0qfPn1MuvbAAw9Ir169zNYs+lPbp/o+TQV1Hz5vEee9vhZ4+nuaDurv6KGLKHRhhXdBh+6bp4WcjkN528C64lbnEOqWMEuXLjXX1ASxZ8+eJtE70558ABBqmKMH4JzofnZvvvmmPPbYY+a5bkB8zz33mMUZCxcuNMWWtmY1fdPETtM7vbVZ6dKl5YMPPjBbsOhPpVud6CpaTdr09mi6+ELbtdlpAahFnl5XN2r2jkHbs3fffbd5rgWcLrRQWhTqLdK0CMx+SzQvfb19+/amyNPCVG/blvuWagAQqrjXLYACpemZJnY65y43Lepyt0i1aMursNKCS9uw3HcWAM4ehR4AAIClmKMHAABgKQo9AAAAS1HoAQAAWIpCDwAAwFIUegAAAJai0AMAALAUhR4AAIClKPQAAADETv8LCYG2ic7wZ9MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练 Neural Network 分类器...\n",
      "Neural Network 准确率: 0.7222\n",
      "Neural Network 精确率: 0.6667\n",
      "Neural Network 召回率: 0.8000\n",
      "Neural Network F1分数: 0.7273\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.79      0.66      0.72        29\n",
      "         ASD       0.67      0.80      0.73        25\n",
      "\n",
      "    accuracy                           0.72        54\n",
      "   macro avg       0.73      0.73      0.72        54\n",
      "weighted avg       0.73      0.72      0.72        54\n",
      "\n",
      "Neural Network 准确率: 0.7222\n",
      "Neural Network 精确率: 0.6667\n",
      "Neural Network 召回率: 0.8000\n",
      "Neural Network F1分数: 0.7273\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.79      0.66      0.72        29\n",
      "         ASD       0.67      0.80      0.73        25\n",
      "\n",
      "    accuracy                           0.72        54\n",
      "   macro avg       0.73      0.73      0.72        54\n",
      "weighted avg       0.73      0.72      0.72        54\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练 Decision Tree 分类器...\n",
      "Decision Tree 准确率: 0.7037\n",
      "Decision Tree 精确率: 0.6667\n",
      "Decision Tree 召回率: 0.7200\n",
      "Decision Tree F1分数: 0.6923\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.74      0.69      0.71        29\n",
      "         ASD       0.67      0.72      0.69        25\n",
      "\n",
      "    accuracy                           0.70        54\n",
      "   macro avg       0.70      0.70      0.70        54\n",
      "weighted avg       0.71      0.70      0.70        54\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练 Gradient Boosting 分类器...\n",
      "Gradient Boosting 准确率: 0.7778\n",
      "Gradient Boosting 精确率: 0.7407\n",
      "Gradient Boosting 召回率: 0.8000\n",
      "Gradient Boosting F1分数: 0.7692\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.81      0.76      0.79        29\n",
      "         ASD       0.74      0.80      0.77        25\n",
      "\n",
      "    accuracy                           0.78        54\n",
      "   macro avg       0.78      0.78      0.78        54\n",
      "weighted avg       0.78      0.78      0.78        54\n",
      "\n",
      "Gradient Boosting 准确率: 0.7778\n",
      "Gradient Boosting 精确率: 0.7407\n",
      "Gradient Boosting 召回率: 0.8000\n",
      "Gradient Boosting F1分数: 0.7692\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.81      0.76      0.79        29\n",
      "         ASD       0.74      0.80      0.77        25\n",
      "\n",
      "    accuracy                           0.78        54\n",
      "   macro avg       0.78      0.78      0.78        54\n",
      "weighted avg       0.78      0.78      0.78        54\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练 AdaBoost 分类器...\n",
      "AdaBoost 准确率: 0.8148\n",
      "AdaBoost 精确率: 0.8261\n",
      "AdaBoost 召回率: 0.7600\n",
      "AdaBoost F1分数: 0.7917\n",
      "\n",
      "分类报告:\n",
      "AdaBoost 准确率: 0.8148\n",
      "AdaBoost 精确率: 0.8261\n",
      "AdaBoost 召回率: 0.7600\n",
      "AdaBoost F1分数: 0.7917\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          TD       0.81      0.86      0.83        29\n",
      "         ASD       0.83      0.76      0.79        25\n",
      "\n",
      "    accuracy                           0.81        54\n",
      "   macro avg       0.82      0.81      0.81        54\n",
      "weighted avg       0.82      0.81      0.81        54\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定义要评估的分类器\n",
    "classifiers = {\n",
    "    'SVM': SVC(probability=True, random_state=42),\n",
    "    'Random Forest': RandomForestClassifier(random_state=42),\n",
    "    'Logistic Regression': LogisticRegression(max_iter=1000, random_state=42),\n",
    "    'KNN': KNeighborsClassifier(),\n",
    "    'Neural Network': MLPClassifier(max_iter=1000, random_state=42),\n",
    "    'Decision Tree': DecisionTreeClassifier(random_state=42),\n",
    "    'Gradient Boosting': GradientBoostingClassifier(random_state=42),\n",
    "    'AdaBoost': AdaBoostClassifier(random_state=42)\n",
    "}\n",
    "\n",
    "# 用于存储每个分类器的性能指标\n",
    "results = {}\n",
    "\n",
    "# 训练并评估每个分类器\n",
    "for name, clf in classifiers.items():\n",
    "    print(f\"\\n训练 {name} 分类器...\")\n",
    "    \n",
    "    # 训练分类器\n",
    "    clf.fit(X_train_scaled, y_train)\n",
    "    \n",
    "    # 在测试集上进行预测\n",
    "    y_pred = clf.predict(X_test_scaled)\n",
    "    \n",
    "    # 计算性能指标\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    \n",
    "    # 存储结果\n",
    "    results[name] = {\n",
    "        'accuracy': accuracy,\n",
    "        'precision': precision,\n",
    "        'recall': recall,\n",
    "        'f1': f1,\n",
    "        'classifier': clf,  # 保存训练好的分类器\n",
    "        'predictions': y_pred  # 保存预测结果\n",
    "    }\n",
    "    \n",
    "    # 输出性能指标\n",
    "    print(f\"{name} 准确率: {accuracy:.4f}\")\n",
    "    print(f\"{name} 精确率: {precision:.4f}\")\n",
    "    print(f\"{name} 召回率: {recall:.4f}\")\n",
    "    print(f\"{name} F1分数: {f1:.4f}\")\n",
    "    \n",
    "    # 输出分类报告\n",
    "    print(\"\\n分类报告:\")\n",
    "    print(classification_report(y_test, y_pred, target_names=['TD', 'ASD']))\n",
    "    \n",
    "    # 计算混淆矩阵\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    # 绘制混淆矩阵\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['TD', 'ASD'], yticklabels=['TD', 'ASD'])\n",
    "    plt.title(f'{name} 混淆矩阵')\n",
    "    plt.ylabel('真实标签')\n",
    "    plt.xlabel('预测标签')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14201d4c",
   "metadata": {},
   "source": [
    "## 7. 模型评估与对比\n",
    "\n",
    "我们将对所有分类算法的性能进行比较分析，并选择最佳的模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "12fd4b9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各分类器性能指标比较 (按准确率排序):\n",
      "                     Accuracy  Precision  Recall  F1 Score\n",
      "Logistic Regression  0.814815   0.800000    0.80  0.800000\n",
      "AdaBoost             0.814815   0.826087    0.76  0.791667\n",
      "Gradient Boosting    0.777778   0.740741    0.80  0.769231\n",
      "SVM                  0.777778   0.740741    0.80  0.769231\n",
      "Random Forest        0.759259   0.750000    0.72  0.734694\n",
      "KNN                  0.722222   0.692308    0.72  0.705882\n",
      "Neural Network       0.722222   0.666667    0.80  0.727273\n",
      "Decision Tree        0.703704   0.666667    0.72  0.692308\n",
      "\n",
      "性能最佳的分类器 (基于F1分数): Logistic Regression\n",
      "准确率: 0.8148\n",
      "F1分数: 0.8000\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 提取性能指标进行比较\n",
    "performance_df = pd.DataFrame({\n",
    "    name: {\n",
    "        'Accuracy': results[name]['accuracy'],\n",
    "        'Precision': results[name]['precision'],\n",
    "        'Recall': results[name]['recall'],\n",
    "        'F1 Score': results[name]['f1']\n",
    "    }\n",
    "    for name in results\n",
    "}).T\n",
    "\n",
    "# 按准确率排序\n",
    "performance_df = performance_df.sort_values('Accuracy', ascending=False)\n",
    "\n",
    "# 显示性能指标比较\n",
    "print(\"各分类器性能指标比较 (按准确率排序):\")\n",
    "print(performance_df)\n",
    "\n",
    "# 找出性能最好的分类器（按F1分数）\n",
    "best_classifier_name = performance_df['F1 Score'].idxmax()\n",
    "best_classifier = results[best_classifier_name]['classifier']\n",
    "best_accuracy = performance_df.loc[best_classifier_name, 'Accuracy']\n",
    "best_f1 = performance_df.loc[best_classifier_name, 'F1 Score']\n",
    "\n",
    "print(f\"\\n性能最佳的分类器 (基于F1分数): {best_classifier_name}\")\n",
    "print(f\"准确率: {best_accuracy:.4f}\")\n",
    "print(f\"F1分数: {best_f1:.4f}\")\n",
    "\n",
    "# 可视化性能比较\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# 准确率比较\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.barplot(x=performance_df.index, y=performance_df['Accuracy'])\n",
    "plt.title('各分类器准确率比较')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylim(0.5, 1)\n",
    "\n",
    "# F1分数比较\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.barplot(x=performance_df.index, y=performance_df['F1 Score'])\n",
    "plt.title('各分类器F1分数比较')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylim(0.5, 1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 如果最佳分类器是随机森林或决策树，查看特征重要性\n",
    "if best_classifier_name in ['Random Forest', 'Decision Tree', 'Gradient Boosting']:\n",
    "    # 获取特征重要性\n",
    "    importances = best_classifier.feature_importances_\n",
    "    \n",
    "    # 创建特征重要性DataFrame\n",
    "    feature_importance_df = pd.DataFrame({\n",
    "        'Feature': X.columns,\n",
    "        'Importance': importances\n",
    "    }).sort_values('Importance', ascending=False)\n",
    "    \n",
    "    # 显示最重要的10个特征\n",
    "    print(f\"\\n{best_classifier_name} 模型的特征重要性 (前10个):\")\n",
    "    print(feature_importance_df.head(10))\n",
    "    \n",
    "    # 可视化特征重要性\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.barplot(x='Importance', y='Feature', data=feature_importance_df.head(10))\n",
    "    plt.title(f'{best_classifier_name} - 特征重要性 (前10个)')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "asd_td_venv",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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